Indiana University-Purdue University Indianapolis (IUPUI) / Multi-Protocol Label Switching (MPLS) is an emerging technology which is the initial step for the forthcoming generation of communication. It uses Labels in order to identify the packets unlike the conventional IP Routing Mechanism which uses the routing table at each router to route the packet. MPLS uses the techniques of FRR with the help of RSVP/CR-LDP to overcome the link and/or node failures in the network. On the other hand there are certain limitations/drawbacks of using the above mechanisms for Failure Detection and Recovery which are multiple protocols such as RSVP/CR-LDP over OSPF/IS-IS and complex algorithms to generate backup paths since each router works individually in order to create a backup tunnel. So to overcome the listed limitations, this paper discusses a new technique for MPLS Networks which is Source Routing \cite{48}. Source Routing is the technique in which the source plays the role of directing the packet to the destination and no other router plays the role of routing the packet in the network. Using the OPNET Modeler 17.5 tool for implementing source routing when there is a network failure is performed and the results are compared by implementing RSVP/CR-LDP over the same failed network. The comparative results show that the network performance is best in the case of Source Routing implementation as compared to the RSVP and CR-LDP signaling over the MPLS Networks.

Satisfactory public transportation (PT) should enable people to reach attractive destinations and desired activities fast, comfortably, safely, and affordably. When PT fails to do so it will have negative effects on the overall accessibility in a society. Evaluating a PT system essentially means measuring to what extent the demand from the users is met, and for such an analysis understanding the concept of accessibility is paramount. Whether an individual will experience a high or a low level of accessibility will likely depend on their personal capabilities, as well as on the surrounding environment. Barriers obstructing an individual from using PT could for example be of physical of phycological nature or come in the shape of public space management disproportionally favoring certain groups of society. Low accessibility can thus be linked to social exclusion, since when a person cannot reach important destinations, their chances to participate in society will be subdued. To measure the accessibility of a PT system, and how a PT system affects the overall accessibility of a destination, it is common practice to use indicators that can represent different categories of social exclusion. This approach was the basis for constructing the performance measurement tool called Bus Stop Ranking Algorithm (BSRA) which was created in the QGIS application Graphical Modeler. BSRA calculates the usefulness of bus stops by counting the number of vulnerable groups, the number of workplaces, and the total population within comfortable walking distance from bus stops, as well as comparing travel times by car and bicycle from residential areas to important locations. The tool was ordered by a private PT company which will use it to make decisions regarding e.g., creating new bus stops, or for relocating, removing, or redesigning existing bus stops or bus routes. The Swedish municipality Lidingö was used as the study area to demonstrate how to use BSRA and how to interpret its output. Using equal weights for all indicators, it was discovered that 9 bus stops in the southern part of Lidingö could be regarded as particularly useful compared to the other 207 bus stops in the municipality. Variables such as the space-temporal component, i.e., changes during the day were not used. Socio economic factors such as segregation were also not highlighted, since all indicators had the same effect on the total scores. Adjusting the weights for some indicators could expose underlying dynamics affecting the total scores for the bus stops and help the PT company make design changes where they will be needed the most.

This master’s thesis deals abou the transmission of voice in communications networks. The theoretical part describes criteria for optimizing voice, such as quality of service, type of service, level of service, service type, and mean opinion score. Next I describe the Internet Protocol, comparing IPv4 and IPv6, VoIP, including security, protocols and parameters necessary for transmission. Other part is about neural networks. There are basically described the neural network, Hopfield neural network and Kohenen neural network. The research is based on a comparison of the network without ensuring the quality of service and with ensuring quality of service. Then, there are compared two types of switches. Classical switch-controlled sequentially, and switch controlled by neural networks. The overall simulation program is implemented in Opnet Modeler. The conclusion deals with the creation of laboratory tasks in this program to compare the different systems of ensuring quality of service.

This work is in the introduction focused on the introduction of technologies for electrical energy storage, their description and capturing the main advantages and disadvantages. After this capture follows comparison of the various technologies in terms of efficiency , discharge time and the price for1 kWh . Following section focuses on electrochemical model VRB batteries , which describes the equations describing the behavior of a battery depending on the chemical an electrical properties . In the penultimate chapter, I introduced the language of object- oriented modeling language Modelica and the most common programs based on it, including a short introduction for modeling in MathModelica. The last part deals with the modeling of specific VRB battery, which we have at the faculty.

In this paper, a study is conducted to investigate the use of Curriculum Learning as an approach to address accuracy issues in a neural network caused by training on a Long-Tailed dataset. The thesis problem is presented by a Swedish e-commerce company. Currently, they are using a neural network that has been modified by them using a CORAL framework. This adaptation means that instead of having a classic binary regression model, it is an ordinal regression model. The data used for training the model has a Long-Tail distribution, which leads to inaccuracies when predicting a price distribution for items that are part of the tail-end of the data. The current method applied to remedy this problem is Re-balancing in the form of down-sampling and up-sampling. A linear training scheme is introduced, increasing in increments of $10\%$ while applying Curriculum Learning. As a method for sorting the data in an appropriate way, inspiration is drawn from Knowledge Distillation, specifically the Teacher-Student model approach. The teacher models are trained as specialists on three different subsets, and furthermore, those models are used as a basis for sorting the data before training the student model. During the training of the student model, the Curriculum Learning approach is used. The results show that for Imbalance Ratio, Kullback-Liebler divergence, Class Balance, and the Gini Coefficient, the data is clearly less Long-Tailed after dividing the data into subsets. With the correct settings before training, there is also an improvement in the training speed of the student model compared to the base model. The accuracy for both the student model and the base model is comparable. There is a slight advantage for the base model when predicting items in the head part of the data, while the student model shows improvements for items that are between the head and the tail. / I denna uppsats genomförs en studie för att undersöka användningen av Curriculum Learning som en metod för att hantera noggrannhetsproblem i ett neuralt nätverk som är en konsekvens av träning på data som har en Long-Tail fördelning. Problemstälnningen som behandlas i uppsatsen är tillhandagiven av ett svensk e-handelsföretag. För närvarande använder de ett neuralt nätverk som har modifierats med hjälp av ett CORAL-ramverk. Denna anpassning innebär att det istället för att ha en klassisk binär regressionsmodell har en ordinal regressionsmodell. Datan som används för att träna modellen har en Long-Tail fördelning, vilket leder till problem vid prediktering av prisfördelning för diverse föremål som tillhör datans svans. Den nuvarande metod som används för att åtgärda detta problem är en Re-balancing i form av down-sampling och up-sampling. Ett linjärt träningschema introduceras, som ökar i steg om $10\%$ medan Curriculum Learning tillämpas. Metoden för att sortera datan på ett lämpligt sätt inspires av Knowledge-Distillation, mer specifikt lärar-elevmodell delen. Lärarmodellerna tränas som specialister på tre olika delmängder, och därefter används dessa modeller som grund för att sortera datan innan tränandet av elevmodellen. Under träningen av elevmodellen tillämpas Curriculum Learning. Resultaten visar att för Imbalance Ratio, Kullback-Libler-divergens, Class Balance och Gini-koefficienten är datat tydligt mindre Long-Tailed efter att datat delats in i delmängder. Med rätt inställningar innan tränandet finns även en förbättring i träningshastighet för elevmodellen jämfört med basmodellen. Noggrannheten för både elevmodellen och basmodellen är jämförbar. Det finns en liten fördel för basmodellen vid prediktering av föremål i huvuddelen av datan, medan elevmodellen visar förbättringar för föremål som ligger mellan huvuddelen och svansen.