Armands Kviesis

Dynamics of Weight Change and Temperature of Apis mellifera (Hymenoptera: Apidae) Colonies in a Wintering Building With Controlled Temperature

Abstract Honey bee wintering in a wintering building (indoors) with controlled microclimate is used in some cold regions to minimize colony losses due to the hard weather conditions. The behavior and possible state of bee colonies in a dark room, isolated from natural environment during winter season, was studied by indirect temperature measurements to analyze the expression of their annual rhythm when it is not affected by ambient temperature, rain, snow, wind, and daylight. Thus, the observed behavior in the wintering building is initiated solely by bee colony internal processes. Experiments were carried out to determine the dynamics of temperature above the upper hive body and weight dynamics of indoors and outdoors wintered honey bee colonies and their brood-rearing performance in spring. We found significantly lower honey consumption-related weight loss of indoor wintered colonies compared with outdoor colonies, while no significant difference in the amount of open or sealed brood was found, suggesting that wintering building saves food and physiological resources without an impact on colony activity in spring. Indoor wintered colonies, with or without thermal insulation, did not have significant differences in food consumption and brood rearing in spring. The thermal behavior and weight dynamics of all experimental groups has changed in the middle of February possibly due to increased brood-rearing activity. Temperature measurement above the upper hive body is a convenient remote monitoring method of wintering process. Predictability of food consumption in a wintering building, with constant temperature, enables wintering without oversupply of wintering honey.

Bus Arrival Time Prediction with Limited Data Set using Regression Models.

The increase of population has intensified everyday rush. Traffic congestions are still a problem in cities and are one of the main cause for public transport delays. City residents and visitors have experienced time loss by using public transport buses, because of waiting at the bus stops and not knowing if the bus is delayed or already serviced the stop. Therefore it is valuable for people to know at what time the bus should arrive (or is it already missed) at specific bus stop. Real-time public bus tracking and management system development has been the focus of many researchers, and many studies have been done in this area. This paper focuses on bus travel time prediction comparison between linear regression and support vector regression models (SVR), when using limited data set. Data were limited in a way that only historical GPS (Global Positioning System) coordinates of bus location (recorded each 30 seconds) and driven distance were used, there were no information about arrival/departure times, delays or dwell times. Distance between stops and delay (assumed values based on route observations by authors) were used as inputs for both models. It was concluded that SVR algorithm showed better results, but the difference was not significantly large.

Enhancing with EV Charging Station Functions a Residential RES based Network.

The emergence of Electric Vehicles is creating a possible congestion of the electric grid. The switch in transportation, especially in cities (future Smart Cities are considered) is asking for the utilization of Renewable Energy Sources, RES, to decrease pollution. To address these two demands the paper proposes a solution based on a Residential Charging station architecture for Urban Electric Vehicles. The theoretical structure is presented and then the practical solution, as Smart Residential MicroGrid based on RES, is shown. In order to make an implementation more economically and technically affordable and be able to address in the very near future the growing need of EV Charging stations, the presented solution starts from the existing equipment used in millions of homes, mainly for solar energy.

Traffic Monitoring System Development in Jelgava City, Latvia.

Smart traffic management and monitoring is one of the key aspects of the modern smart city. Traffic flow estimation is crucial for sustainable traffic planning in the city. A requirement for successful planning and optimization of traffic is vehicle counting on the streets. Surveillance video is a suitable data source for precise vehicle counting. A solution for real-time vehicle traffic monitoring, tracking and counting is proposed in Jelgava city, Latvia. It is based on motion detection using background modeling, which is enhanced by statistical analysis. Two-phase assessment is utilized: motion blobs are detected and tracked using custom state machine implementation, then tracking results are passed through number of statistical filters to eliminate false positive detections. The system demonstrates good performance and acceptable accuracy on given test cases (about 97% accuracy for regular traffic conditions).

Development of the Web Platform for Management of Smart Charging Stations for Electric Vehicles.

Shortage of fossil fuels and ecological thinking leads to shift in technologies for vehicle production. In the future only electric vehicles (EVs) would be produced. This will lead to huge increase in number of EVs worldwide, so it would be crucial to provide a broad public charging infrastructure. This paper exactly concentrates on the essential role of infrastructure in the mass implementation of electric vehicles. A focus is placed on sharing the residential infrastructure for public usage. Paper describes authors developed Web platform for sharing the information about privately owned charging stations, describing the additional option to link station hardware with software for real-time charging data and station availability updates. Developed platform brings together drivers of EVs and owners of the infrastructure. Developed platform is built like an interactive map, based on Google Maps service. Together with software part, authors developed also hardware, which is one Microgrid based on renewable energy sources with EV charging station functionality.

Implementation of Smart Parking Solution by Image Analysis.

Modern smart city concept implies various smart aspects including smart parking management. Searching for a free parking lot can be a challenging task, especially during major events, therefore automatic system, which will help drivers to find a free parking is very valuable. There are many intrusive and non-intrusive technologies available for smart parking development, but authors of this paper developed a system based on video processing and analysis. Authors developed Python application for real-time parking lot monitoring based on video analysis of public video stream. Five classifier models (Logistic Regression, Linear Support Vector Machine, Radial Basis Function Support Vector Machine, Decision Tree and Random Forest) were compared for parking lot occupancy detection. Logistic regression classifier showed better results and was chosen for real-time parking monitoring application. System shows good performance and correctly predicted parking lot occupancy almost in all test cases.