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  • 1.
    Cederlund, Harald
    et al.
    SLU, Institutionen för mikrobiologi.
    Fogelberg, Fredrik
    Institutet för jordbruks- och miljöteknik (JTI).
    Hansson, David
    SLU, Institutionen för biosystem och teknologi.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Schroeder, Håkan
    SLU, Institutionen för landskapsarkitektur, planering och förvaltning.
    Utveckling av metod för att bedöma behovet av ogräsbekämpning i spår2014Rapport (Övrigt vetenskapligt)
    Abstract [sv]

    En miljöanpassad och resurseffektiv hantering av ogräs i spår ställer krav på god kunskap om vegetationsförhållanden för att behovsanpassa bekämpningsinsatserna. En automatiserad registrering skulle kunna utgöra ett komplement till dagens manuella inspektioner och skulle över tiden helt eller delvis kunna ersätta dessa. En utmaning är att hitta en metod som ger en rimlig upplösning i informationen som samlas in, så att den kan hanteras rationellt av berörda aktörer och samtidigt utgöra ett beslutsunderlag med tillräcklig precision. Projektet studerade två automatiserade metoder som kan vara aktuella för Trafikverket att använda i framtiden: 1) Machine vision metoden utnyttjar kamerasensorer för att känna av sin omgivning i det synliga respektive nära infraröda spektrumet. 2) N-sensorn sänder ut ljus inom det område som reflekteras av växternas klorofyll. Mängden klorofyll ger ett mätvärde som kan korreleras till biomassan. Valet av teknik beror på vad informationen ska användas till. Om syftet är att översiktligt kartlägga vegetationsförekomst i spår, för att planera åtgärder för underhåll, kan N-sensortekniken vara lämplig. Om man över ytan och tiden vill övervaka och kartlägga aktuell och precis vegetationsstatus, för att kunna bekämpa utvald vegetation med rätt insats, är machine vision tekniken bättre lämpad. Såväl machine vision metoden som N-sensortekniken bygger på registrering av data tillsammans med en GPS-positionering. På sikt kan denna information läggas i databaser som är direkt åtkomliga för berörda organisationer och t o m online i fält under eller i samband med en bekämpningsåtgärd. De två teknikerna jämfördes med manuella (visuella) skattningar av ogräsförekomsten. Den visuella skattningen av yttäckningsgrad av ogräs i fält skiljde sig statistiskt mellan olika bedömare. När det gäller att uppskatta frekvensen (antalet) vedartade växter (träd och buskar) inom provytorna så var observatörerna relativt överens. Samma person är ofta konsekvent i sitt bedömande, men att jämföra med andra personers bedömning kan ge missvisande resultat. Systemet för användning av informationen om ogräsförekomst behöver utvecklas som helhet. Tröskelvärden för hur mycket ogräs som kan tolereras på olika typer av spår/driftsplatser är en viktig komponent i ett sådant system. Klassificeringssystemet ska kunna hantera de krav som ställs för att säkerställa banans kvalitet och olika förutsättningar som trafikförhållanden, platsgivna förutsättningar för banan och vegetationens egenskaper. Projektet rekommenderar Trafikverket att: diskutera hur tröskelvärden för vegetationsförekomst på spår kan fastställas  genomföra registrering av vegetationsförekomst över längre och fler sträckor med en eller flera av de metoder som studerats i projektet inleda införande av system som effektivt kopplar informationen om vegetation till position inkludera förekomst av vegetation i den registrering som idag sker av spårens (banans) tekniska kvalitet och ansluta datamaterialet till övriga underhållsrelaterade databaser inrätta ett antal representativa ytor där ogräsfloran på spåren regelbundet inventeras och mäts för att få en bild av den långsiktiga utveckling som grund för säkrare prognoser för vegetationsutveckling säkerställa att nödvändiga utbildningsinsatser genomförs

  • 2.
    Laryea, Rueben
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Carling, Kenneth
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Cialani, Catia
    Högskolan Dalarna, Akademin Industri och samhälle, Nationalekonomi.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Sensitivity analysis of a risk classification model for food price volatility2018Ingår i: International Journal of Risk Assessment and Management, ISSN 1466-8297, E-ISSN 1741-5241, Vol. 21, nr 4, s. 374-382Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A sensitivity analysis to vary the weights of an accurate predictive classification model to produce a mixed model for ranking countries on the risk of food price volatility is carried out in this paper. The classification model is a marginal utility function consisting of multiple criteria. The aim of the sensitivity analysis is to derive a mixed model to be used in ranking of country alternatives to aid in policy formulation. Since in real-life situations the data that goes into decision making could be subjected to possibilities of alterations over time, it is essential to aid decision makers to vary the weights of the criteria using both subjective and objective information to introduce imprecision and to generate relative values of the criteria with a scale to form a mixed model. The mixed model can be used to rank future relative alternative value data sets for policy formulation.

  • 3.
    Laryea, Rueben
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Farsari, Ioanna
    Högskolan Dalarna, Akademin Industri och samhälle, Turismvetenskap.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    A Decision Tool Approach to Sensitivity Analysis in a Risk Classification Model2018Ingår i: Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A Decision Analytical tool capable of handling numerically imprecise data for decision making is used in this paper to analyze the risk of the effect of data alteration in the ranking positions of country alternatives for food price volatility. Unguided decision making processes would lead to non-optimal decisions with it’s dire consequences on the resources of organizations. The paper is thus guided by the use of an accurate risk classification model to implement uncertainty and imprecision which are essential part of real life decision making processes with computer based tools to overcome the problem of possibilities uncertain and imprecise input data of criteria and alternatives. A ranking of the alternatives is conducted after imprecision is considered in the input data and a further analysis is carried out to determine which criteria is sensitive enough to alter the position of a country in the rankings.

  • 4. Meszyński, Sebastian
    et al.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Agent-based modelling and simulation of insulin-glucose subsystem2016Ingår i: Proceedings of the Fifth International Conference on Intelligent Systems and Applications, 2016, s. 63-68Konferensbidrag (Refereegranskat)
    Abstract [en]

    Mathematical analytical modeling and computer simulation of the physiological system is a complex problem with great number of variables and equations. The objective of this research is to describe the insulin-glucose subsystem using multi-agent modeling based on intelligence agents. Such an approach makes the modeling process easier and clearer to understand; moreover, new agents can be added or removed more easily to any investigations. The Stolwijk-Hardy mathematical model is used in two ways firstly by simulating the analytical model and secondly by dividing up the same model into several agents in a multiagent system. In the proposed approach a multi-agent system was used to build a model for glycemic homeostasis. Agents were used to represent the selected elements of the human body that play an active part in this process. The experiments conducted show that the multi-agent model has good temporal stability with the implemented behaviors, and good reproducibility and stability of the results. It has also shown that no matter what the order of communication between agents, the value of the result of the simulation was not affected. The results obtained from using the framework of multi-agent system actions were consistent with the results obtained with insulin-glucose models using analytical modeling.

  • 5.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    A machine learning approach for recognising woody plants on railway trackbeds2016Ingår i: International Conference on Railway Engineering (ICRE 2016), 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    The purpose of this work in progress study was to test the concept of recognising plants using images acquired by image sensors in a controlled noise-free environment. The presence of vegetation on railway trackbeds and embankments presents potential problems. Woody plants (e.g. Scots pine, Norway spruce and birch) often establish themselves on railway trackbeds. This may cause problems because legal herbicides are not effective in controlling them; this is particularly the case for conifers. Thus, if maintenance administrators knew the spatial position of plants along the railway system, it may be feasible to mechanically harvest them. Primary data were collected outdoors comprising around 700 leaves and conifer seedlings from 11 species. These were then photographed in a laboratory environment. In order to classify the species in the acquired image set, a machine learning approach known as Bag-of-Features (BoF) was chosen. Irrespective of the chosen type of feature extraction and classifier, the ability to classify a previously unseen plant correctly was greater than 85%. The maintenance planning of vegetation control could be improved if plants were recognised and localised. It may be feasible to mechanically harvest them (in particular, woody plants). In addition, listed endangered species growing on the trackbeds can be avoided. Both cases are likely to reduce the amount of herbicides, which often is in the interest of public opinion. Bearing in mind that natural objects like plants are often more heterogeneous within their own class rather than outside it, the results do indeed present a stable classification performance, which is a sound prerequisite in order to later take the next step to include a natural background. Where relevant, species can also be listed under the Endangered Species Act.

  • 6.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik. Edinburgh Napier University.
    Automating condition monitoring of vegetation on railway trackbeds and embankments2016Doktorsavhandling, monografi (Övrigt vetenskapligt)
    Abstract [en]

    Vegetation growing on railway trackbeds and embankments present potential problems. The presence of vegetation threatens the safety of personnel inspecting the railway infrastructure. In addition vegetation growth clogs the ballast and results in inadequate track drainage which in turn could lead to the collapse of the railway embankment.

    Assessing vegetation within the realm of railway maintenance is mainly carried out manually by making visual inspections along the track. This is done either on-site or by watching videos recorded by maintenance vehicles mainly operated by the national railway administrative body.

    A need for the automated detection and characterisation of vegetation on railways (a subset of vegetation control/management) has been identified in collaboration with local railway maintenance subcontractors and Trafikverket, the Swedish Transport Administration (STA). The latter is responsible for long-term planning of the transport system for all types of traffic, as well as for the building, operation and maintenance of public roads and railways.

    The purpose of this research project was to investigate how vegetation can be measured and quantified by human raters and how machine vision can automate the same process.

    Data were acquired at railway trackbeds and embankments during field measurement experiments. All field data (such as images) in this thesis work was acquired on operational, lightly trafficked railway tracks, mostly trafficked by goods trains. Data were also generated by letting (human) raters conduct visual estimates of plant cover and/or count the number of plants, either on-site or in-house by making visual estimates of the images acquired from the field experiments. Later, the degree of reliability of(human) raters’ visual estimates were investigated and compared against machine vision algorithms.

    The overall results of the investigations involving human raters showed inconsistency in their estimates, and are therefore unreliable. As a result of the exploration of machine vision, computational methods and algorithms enabling automatic detection and characterisation of vegetation along railways were developed. The results achieved in the current work have shown that the use of image data for detecting vegetation is indeed possible and that such results could form the base for decisions regarding vegetation control. The performance of the machine vision algorithm which quantifies the vegetation cover was able to process 98% of the im-age data. Investigations of classifying plants from images were conducted in in order to recognise the specie. The classification rate accuracy was 95%.Objective measurements such as the ones proposed in thesis offers easy access to the measurements to all the involved parties and makes the subcontracting process easier i.e., both the subcontractors and the national railway administration are given the same reference framework concerning vegetation before signing a contract, which can then be crosschecked post maintenance.A very important issue which comes with an increasing ability to recognise species is the maintenance of biological diversity. Biological diversity along the trackbeds and embankments can be mapped, and maintained, through better and robust monitoring procedures. Continuously monitoring the state of vegetation along railways is highly recommended in order to identify a need for maintenance actions, and in addition to keep track of biodiversity. The computational methods or algorithms developed form the foundation of an automatic inspection system capable of objectively supporting manual inspections, or replacing manual inspections.

  • 7.
    Nyberg, Roger G.
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik. Edinburgh Napier University.
    Gupta, Narendra K.
    Edinburgh Napier University.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Machine vision for condition monitoring vegetation on railway embankments2015Ingår i: 6th IET Conference on Railway Condition Monitoring (RCM 2014), The Institution of Engineering and Technology (IET) , 2015, s. 3.2.1-3.2.1Konferensbidrag (Refereegranskat)
    Abstract [en]

    National Railway Administrations in Northern Europe do not employ systematic procedures in monitoring the current state of vegetation to form the basis of maintenance decision making. Current day vegetation maintenance is largely based on human visual estimates. This paper investigates a machine vision (MV) approach to be able to automatically quantify the amount of vegetation on a given railway section. An investigation assessing the reliability of human estimates is also conducted along the same railway section.A machine vision algorithm was developed and implemented. Initially, the algorithm determines a region of interest (ROI), i.e. the desired monitored area in each collected image. This ROI is dependent on fixed objects in the image, namely the two rails. When the rails are found the algorithm will compute the ROI, which is predetermined by e.g. the railway administrator. After this, a perspective projection correction will be made, and the vegetation will be segmented. Cover is reported as a percentage of the total ROI for each image. Results: The machine vision algorithm is capable of processing 98% of the images. Failure in the remaining 2% of cases is attributed to the algorithms' inability in find the rails within the image. Analysis of variance tests were conducted to compare the observers cover assessments in sample plots. Upon comparing the observers plot wise mean estimates with the machine vision output, results show that the human visual estimates do not correlate with the results reported by the machine vision output. As such, the result indicates that it is very hard to fit human estimates by regression with the machine vision result. Additionally the results show that humans are not in agreement with each other, and often are exaggerating the extent of vegetation cover compared to the machine vision output.The investigation shows that one should be very careful when trusting/interpreting human visual estimates. In conclusion, based on the results, the automated machine vision solution is proposed as complementing, or replacing, manual human inspections serving as a base for vegetation control decisions. Impact: By objectively measuring the quantity of vegetation, the maintenance planning and procurement can be effectively improved over time. A machine vision approach for condition monitoring of vegetation will enable condition based maintenance with prior consideration on issues mainly relevant to vegetation type, quantity and biodiversity.

  • 8.
    Nyberg, Roger G.
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik. School of Engineering and the Built Environment, Edinburgh Napier University, EH10 5DT Edinburgh, UK.
    Gupta, Narendra K.
    School of Engineering and the Built Environment, Edinburgh Napier University, EH10 5DT Edinburgh, UK.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Monitoring vegetation on railway embankments: supporting maintenance decisions2013Ingår i: Proceedings of the 2013 International Conference on Ecology and Transportation, 2013, s. 1-18Konferensbidrag (Refereegranskat)
    Abstract [en]

    The national railway administrations in Scandinavia, Germany, and Austria mainly resort to manual inspections to control vegetation growth along railway embankments. Manually inspecting railways is slow and time consuming. A more worrying aspect concerns the fact that human observers are often unable to estimate the true cover of vegetation on railway embankments. Further human observers often tend to disagree with each other when more than one observer is engaged for inspection. Lack of proper techniques to identify the true cover of vegetation even result in the excess usage of herbicides; seriously harming the environment and threating the ecology. Hence work in this study has investigated aspects relevant to human variationand agreement to be able to report better inspection routines. This was studied by mainly carrying out two separate yet relevant investigations.First, thirteen observers were separately asked to estimate the vegetation cover in nine imagesacquired (in nadir view) over the railway tracks. All such estimates were compared relatively and an analysis of variance resulted in a significant difference on the observers’ cover estimates (p<0.05). Bearing in difference between the observers, a second follow-up field-study on the railway tracks was initiated and properly investigated. Two railway segments (strata) representingdifferent levels of vegetationwere carefully selected. Five sample plots (each covering an area of one-by-one meter) were randomizedfrom each stratumalong the rails from the aforementioned segments and ten images were acquired in nadir view. Further three observers (with knowledge in the railway maintenance domain) were separately asked to estimate the plant cover by visually examining theplots. Again an analysis of variance resulted in a significant difference on the observers’ cover estimates (p<0.05) confirming the result from the first investigation.The differences in observations are compared against a computer vision algorithm which detects the "true" cover of vegetation in a given image. The true cover is defined as the amount of greenish pixels in each image as detected by the computer vision algorithm. Results achieved through comparison strongly indicate that inconsistency is prevalent among the estimates reported by the observers. Hence, an automated approach reporting the use of computer vision is suggested, thus transferring the manual inspections into objective monitored inspections

  • 9.
    Nyberg, Roger G.
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik. Edinburgh Napier University, Scotland, UK.
    Gupta, Narendra
    Edinburgh Napier University, Scotland, UK.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Detecting Plants on Railway Embankments2013Ingår i: Journal of Software Engineering and Applications, ISSN 1945-3116, E-ISSN 1945-3124, Vol. 6, nr 3B, s. 8-12Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper investigates problems concerning vegetation along railways and proposes automatic means of detecting ground vegetation. Digital images of railway embankments have been acquired and used for the purpose. The current work mainly proposes two algorithms to be able to achieve automation. Initially a vegetation detection algorithm has been investigated for the purpose of detecting vegetation. Further a rail detection algorithm that is capable of identifying the rails and eventually the valid sampling area has been investigated. Results achieved in the current work report satisfactory (qualitative) detection rates.

  • 10.
    Nyberg, Roger G.
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Gupta, Narendra K.
    Edinburgh Napier University.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Inter-rater reliability in determining the types of vegetation on railway trackbeds2015Ingår i: Web Information Systems Engineering – WISE 2015: 16th International Conference, Miami, FL, USA, November 1-3, 2015, Proceedings, Part II / [ed] Wang, J., Cellary, W., Wang, D., Wang, H., Chen, S.-C., Li, T., Zhang, Y., Springer, 2015, Vol. 9419, s. 379-390Konferensbidrag (Refereegranskat)
    Abstract [en]

    Vegetation growing on railway trackbeds and embankments can present several potential problems. Consequently, such vegetation iscontrolled through various maintenance procedures. In order to investigate the extent of maintenance needed, one of the first steps in anymaintenance procedure is to monitor or inspect the railway section in question. Monitoring is often carried out manually by sending out inspectorsor by watching recorded video clips of the section in question.To facilitate maintenance planning, the ability to assess the extent of vegetation becomes important. This paper investigates the reliability ofhuman assessments of vegetation on railway trackbeds.In this study, five maintenance engineers made independent visual estimates of vegetation cover and counted the number of plant clusters fromimages.The test results showed an inconsistency between the raters when it came to visually estimating plant cover and counting plant clusters. The resultsshowed that caution should be exercised when interpreting individual raters’ assessments of vegetation.

  • 11.
    Paidi, Vijay
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Håkansson, Johan
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Smart parking sensors, technologies and applications for open parking lots: a review2018Ingår i: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, nr 8, s. 735-741Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Parking a vehicle in traffic dense environments often leads to excess time of driving in search for free space which leads to congestions and environmental pollution. Lack of guidance information to vacant parking spaces is one reason for inefficient parking behaviour. Smart parking sensors and technologies facilitate guidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensors or technologies is in use for open parking lot. This paper reviews the literature on the usage of smart parking sensors, technologies, applications and evaluate their applicability to open parking lots. Magnetometers, ultrasonic sensors and machine vision were few of the widely used sensors and technologies on closed parking lots. However, this paper suggests a combination of machine vision, convolutional neural network or multi-agent systems suitable for open parking lots due to less expenditure and resistance to varied environmental conditions. Few smart parking applications show drivers the location of common open parking lots. No application provided real time parking occupancy information, which is a necessity to guide them along the shortest route to free space. To develop smart parking applications for open parking lots, further research is needed in the fields of deep learning and multi-agent systems.

  • 12.
    Paidi, Vijay
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Håkansson, Johan
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Smart Parking Tools Suitability for Open Parking Lots: A Review2018Ingår i: Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems, Madeira, 2018, s. 600-609Konferensbidrag (Refereegranskat)
    Abstract [en]

    Parking a vehicle in traffic dense environments is a common issue in many parts of the world which oftenleads to congestion and environmental pollution. Lack of guidance information to vacant parking spaces isone of the reasons for inefficient parking behaviour. Smart parking sensors and technologies facilitateguidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensorsor technologies are in use for the common open parking lot. This paper reviews the literature on the usage ofsmart parking sensors, technologies, applications and evaluate their suitability to open parking lots. Suitabilitywas made in terms of expenditure and reliability. Magnetometers, ultrasonic sensors and machine vision werefew of the widely used sensors and technologies used in closed parking lots. However, this paper suggests acombination of machine vision, fuzzy logic or multi-agent systems suitable for open parking lots due to lessexpenditure and resistance to varied environmental conditions. No application provided real time parkingoccupancy information of open parking lots, which is a necessity to guide them along the shortest route tofree space. To develop smart parking applications for open parking lots, further research is needed in the fieldsof deep learning.

  • 13.
    Paidi, Vijay
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Deep learning-based vehicle occupancy detection in an open parking lot using thermal camera2019Ingår i: Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    Parking vehicle is a daunting task and a common problem in many cities around the globe. The search for parking space leads to congestion, frustration and increased air pollution. Information of a vacant parking space would facilitate to reduce congestion and subsequent air pollution. Therefore, aim of the paper is to acquire vehicle occupancy in an open parking lot using deep learning. Thermal camera was used to collect the data during varying environmental conditions such as; sunny, dusk, dawn, dark and snowy conditions. Vehicle detection with deep learning was implemented where image classification and object localization were performed for multi object detection. The dataset consists of 527 images which were manually labelled as there were no pre-labelled thermal images available. Multiple deep learning networks such as Yolo, ReNet18, ResNet50 and GoogleNet with varying layers and architectures were evaluated on vehicle detection. Yolo, GoogleNet and ResNet18 are computationally efficient detectors which took less processing time while Resnet50 produced better detection results compared to other detectors. However, ResNet18 also produced minimal miss rates and is suitable for real time vehicle detection. The detected results were compared with a template of parking spaces and IoU value is used to identify vehicle occupancy information.

  • 14.
    Paidi, Vijay
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Håkansson, Johan
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    A holistic decision support system for last mile handovers2019Ingår i: Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    The last mile handover is assumed to be the most problematic part in the delivery process and the costs can go upto 50% of the total logistic cost. Real time consumer communication and dynamic scheduling are the major problem areas associated with effective attended last mile handovers. Therefore, aim of this paper is to report the design and development of a holistic decision support system’s functionalities which simultaneously addresses real time consumer communication and dynamic scheduling. A decision support system was designed and developed based on workshops, expert group interviews and its functionalities were proposed with the use cases. A survey was conducted with consumers of a retailer where majority of the consumers accepted the use of mobile communication devices to enable real time communication and alternate handover location which improves customer satisfaction and facilitates to avoid missed handovers. A pilot test was performed where routing distance was reduced with the use of optimized handover routes. However the improvement is subjected to the experience of driver and real time traffic conditions. We conclude that a holistic decision support system with multi-party communication among the stakeholders facilitates in reducing operational costs for logistic companies and improving customer satisfaction and business opportunities.

  • 15.
    Yella, Siril
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Assessing the quality and reliability of visual estimates in determining plant cover on railway embankments2016Ingår i: Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part II / [ed] Wojciech Cellary, Mohamed F. Mokbel, Jianmin Wang, Hua Wang, Rui Zhou, Yanchun Zhang, 2016, Vol. 10042, s. 404-410Konferensbidrag (Refereegranskat)
    Abstract [en]

    This study has investigated the quality and reliability of manual assessments on railway embankments within the domain of railway maintenance. Manually inspecting vegetation on railway embankments is slow and time consuming. Maintenance personnel also require extensive knowledge of the plant species, ecology and bio-diversity to be able to recommend appropriate maintenance action. The overall objective of the study is to investigate the reliable nature of manual inspection routines in favour an automatic approach. Visual estimates of plant cover reported by domain experts’ have been studied on two separate railway sections in Sweden. The first study investigated visual estimates using aerial foliar cover (AFC) and sub-plot frequency (SF) methods to assess the plant cover on a railway section in Oxberg, Alvdalsbanan, Sweden. The second study investigated visual estimates using aerial canopy cover method on a railway section outside Vetlanda, Sweden. Visual estimates of the domain experts were recorded and analysis-of-variance (ANOVA) tests on the mean estimates were investigated to see whether if there were disagreements between the raters’. ICC(2, 1) was used to study the differences between the estimates. Results achieved in this work indicate statistically significant differences in the mean estimates of cover (p < 0.05) reported by the domain experts on both the occasions.

  • 16.
    Yella, Siril
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Nyberg, Roger G
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Gupta, Narendra K.
    Edinburgh Napier University.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Reliability of manual assessments in determining the types of vegetation on railway tracks2015Ingår i: Web Information Systems Engineering – WISE 2015: 16th International Conference, Miami, FL, USA, November 1-3, 2015, Proceedings, Part II / [ed] Wang, J., Cellary, W., Wang, D., Wang, H., Chen, S.-C., Li, T., Zhang, Y., Springer, 2015, Vol. 9149, s. 391-399Konferensbidrag (Refereegranskat)
    Abstract [en]

    Current day vegetation assessments within railway maintenance are (to a large extent) carried out manually. This study has investigated the reliability of such manual assessments by taking three non-domain experts into account. Thirty-five track images under different conditions were acquired for the purpose. For each image, the raters’ were asked to estimate the cover of woody plants, herbs and grass separately (in %) using methods such as aerial canopy cover, aerial foliar cover and sub-plot frequency. Visual estimates of raters’ were recorded and analysis-of-variance tests on the mean cover estimates were investigated to see whether if there were disagreements between the raters’.  In tra-correl ation coefficient was used to study the differences between the estimates. Results achieved in this work revealed that seven out of the nine analysis-of-variance tests conducted in this study have demonstrated significant difference in the mean estimates of cover (p < 0.05).

  • 17.
    Yella, Siril
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Payvar, Barsam
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Gupta, Narendra
    Edinburgh Napier University.
    Machine vision approach for automating vegetation detection on railway tracks2013Ingår i: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 22, nr 2, s. 179-196Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The presence of vegetation on railway tracks (amongst other issues) threatens track safety and longevity. However, vegetation inspections in Sweden (and elsewhere in the world) are currently being carried out manually. Manually inspecting vegetation is very slow and time consuming. Maintaining an even quality standard is also very difficult. A machine vision-based approach is therefore proposed to emulate the visual abilities of the human inspector. Work aimed at detecting vegetation on railway tracks has been split into two main phases. The first phase is aimed at detecting vegetation on the tracks using appropriate image analysis techniques. The second phase is aimed at detecting the rails in the image to determine the cover of vegetation that is present between the rails as opposed to vegetation present outside the rails. Results achieved in the current work indicate that the machine vision approach has performed reasonably well in detecting the presence/absence of vegetation on railway tracks when compared with a human operator.

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