Modulaarisen hissikuilun suunnittelu

Opinnäytetyön tehtävänä oli suunnitella modulaarinen hissikuilu. Tilaaja oli kiinnostunut kehittämään konseptin jälkiasennettavalle hissikuilulle. Tilaajalla on tavoitteena laaja-alainen hissikuilutuotanto tulevaisuudessa ja hissien tarjoaminen vanhoihin rakennuksiin. Opinnäytetyön tavoitteena oli saada 3D-malli hissikuilusta ja suunnitelma hissikuilun toteutuksesta. Hissikuilun suunnittelulle annettiin lähtökohdat tilaajan toimesta. Tilaaja toimitti käytettävät layout-vaihtoehdot ja käytettävän hissimallin. Rakenteita määritti pitkälti myös se, että kuilun tulee olla jo tehtaalta lähtiessään mahdollisimman valmis. Opinnäytetyö aloitettiin keräämällä taustatietoa ja perehtymällä aiheeseen. Varsinainen kehitystyö alkoi kuilun rungon suunnittelulla, jonka ympärille suunniteltiin muut rakenteet. Runkorakenteen ympärille tutkittiin erilaisia rakennevaihtoehtoja ja suunniteltiin rakenteita sekä liitoksia. Rungon kestävyyttä selvitettiin lujuuslaskelmilla ja rungon rakennetta muokattiin laskelmista saatujen tulosten mukaisiksi. Opinnäytetyön tuloksena syntyi pohja-, julkisivu-, leikkaus- ja runkokuvat sekä rakennetyypit, detaljit ja laskelmat, joista osa on opinnäytetyön liitteenä. Opinnäytetyössä piti luoda jotakin täysin uutta. Haastavaa oli myös se, että oikeata kohdetta hankkeelle ei ollut vielä olemassa, joten lähtötietoja jouduttiin kuvittelemaan. Kaikkia suunnitteluun vaikuttavia asioita ei siis ole saatettu huomioida oikealla tavalla, mutta niihin voidaan ottaa kantaa varsinaisen pilottikohteen suunnittelussa. Suunniteltu virtuaalimalli täyttää sille asetetut tavoitteet ja sitä voidaan hyödyntää varsinaisten kohteiden suunnittelussa.The purpose of this thesis was to design a modular elevator shaft. The client is interested in developing their elevator shaft concept so that the shafts can be installed to finished buildings, especially older buildings that do not have elevators. The client intends to launch production on a large scale because Finland’s population is growing steadily older and there will be more demand for elevators in the future. The aim the thesis was to design a 3D-model of the shaft and to plan how to put it into practice. The client gave some basic guidelines for the designing process, such as the layout alternatives and the model of the elevator. The main idea was that the shaft needs to be as ready as possible before it is brought on site. That ex-cluded some structures that are more suited for in situ construction. The thesis process began by seeking information about elevators in general. The designing itself started with the designing of the framework for the shaft. The stability of the framework was ensured by doing some calculations and the structures were modified accordingly. After the framework was designed, other structures started to take form as well. This thesis includes a floor plan, a front elevation, a side projection and construction drawings. Some details and structural calculations are also included. To meet the client’s demands for this thesis, something that had never been done before had to be created. What also added to the challenge was that any specific building was not pointed out to design the shaft for. Because of this some assumptions had to be made. In case it is necessary, it is possible to make changes when designing the first versions of the shaft. The virtual model that is included here meets the goals of the thesis and it can be utilized when designing the actual shafts

Network-Aware Resource Management for Mobile Cloud

The author proposes a novel system architecture for mobile cloud computing (MCC) that includes a controller for managing computing and communication resources in Cloud Radio Access Network (C-RAN) environment. The gathered monitoring information in the controller is used when making resource allocation/management decisions. A unified protocol has been proposed, which utilises the same packet format for mobile task offloading and resource management. Moreover, the packet format and the message types of the protocol have been presented. An MCC scenario (i.e., cloudlet+clone) that consists of a cloudlet layer has been studied, in which the cloudlets are deployed next to Wi-Fi access points and serve as a localised service point in proximity to mobile devices to improve the performance of mobile cloud services. On top of this, an offloading algorithm is proposed with the main aim of deciding whether to offload to a clone or a cloudlet. The architecture described above has been implemented as a prototype by focussing on resource management in the mobile cloud. A partial implementation of a resource monitoring module that monitors both computing and communication resources have also been presented. Auto-scaling enables efficient computing resource management in the mobile cloud. An empirical performance analysis of cloud vertical scaling for mobile cloud resource management has been conducted. The working procedures of the proposed unified protocol have been illustrated to show the mobile task offloading and resource allocation functions. Simulation results of cloudlet+clone mobile task offloading algorithm demonstrate the effectiveness and efficiency of the presented task offloading architecture, and offloading algorithm on response time and energy consumption. The empirical vertical auto-scaling performance analysis for mobile cloud resource allocation shows that time delays when scaling resources (CPU, RAM, disk) in mobile cloud varies. Moreover, the scaling delay depends on the scaling amount at the given iteration

Communication and Computation Cooperation in Cloud Radio Access Network with Mobile Edge Computing

Cloud radio access network (C-RAN) and mobile edge computing (MEC) have emerged as promising candidates for the next generation access network techniques. Unfortunately, although MEC tries to utilize the highly distributed computing resources in close proximity to user equipments (UE), C-RAN suggests to centralize the baseband processing units (BBU) deployed in radio access networks. To better understand and address such a conflict, this paper closely investigates the MEC task offloading control in C-RAN environments. Most prior work handling offloading control falls in the general category of resource allocation optimization. However in this paper, we focus on the perspective of matching problem. Our model smartly captures the unique features in both MEC and C-RAN with respect to communication and computation efficiency constraints. We divide the cross-layer optimization into the following three stages: (1) matching between remote radio heads (RRH) and UEs, (2) matching between BBUs and UEs, and (3) matching between mobile clones (MC) and UEs. By applying the Gale-Shapley Matching Theory in the duplex matching framework, we propose a multi-stage heuristic to minimize the refusal rate for user's task offloading requests. Trace-based simulation conforms that our solution can successfully achieve near-optimal performance in such a hybrid deployment

Energy-efficient and network-aware offloading algorithm for mobile cloud computing

We propose a new system architecture for mobile cloud computing (MCC) that includes a middle layer sitting between mobile devices and their cloud infrastructure or clones. This middle layer is composed of cloudlets and is thus called a cloudlet layer. Cloudlets are deployed next to IEEE 802.11 access points and serve as a localized service point in close proximity to mobile devices to improve the performance of mobile cloud services. On top of this new architecture, an offloading algorithm is proposed with the main aim of deciding whether to offload to a clone or a cloudlet. The decision-making takes into consideration the energy consumption for task execution and the network status while satisfying certain task response time constraints. We also introduce a data caching mechanism at cloudlets to further improve the overall MCC performance. Simulation results demonstrate the effectiveness and efficiency of the proposed system architecture and offloading algorithm in terms of response time and energy consumption

Cost-effective resource allocation in C-RAN with mobile cloud

Taking full advantages of two cloud-based techniques, i.e., cloud radio access network (C-RAN) and mobile cloud computing (MCC), mobile operators will be able to provide the good service to the mobile user as well as increasing their revenue. This paper aims to minimize the mobile operator's cost while at the same time, meet the task time constraints of the mobile users. In particular, we assume that the mobile cloud first completes the tasks for the mobile user and then transmits the results back to the users through C-RAN. Joint cost-effective resource allocation is proposed between MCC and C-RAN and simulation results confirm that the proposed cost minimization and resource allocation solution outperforms nonoptimal solutions

On Efficient Offloading Control in Cloud Radio Access Network with Mobile Edge Computing

Cloud radio access network (C-RAN) and mobile edge computing (MEC) have emerged as promising candidates for the next generation access network techniques. Unfortunately, although MEC tries to utilize the highly distributed computing resources in close proximity to user equipments equipments (UE), C-RAN suggests to centralize the baseband processing units (BBU) deployed in radio access networks. To better understand and address such a conflict, this paper closely investigates the MEC task offloading control in C-RAN environments. In particular, we focus on perspective of matching problem. Our model smartly captures the unique features in both MEC and C-RAN with respect to communication and computation efficiency constraints. We divide the cross-layer optimization into the following three stages: (1) matching between remote radio heads (RRH) and UEs, (2) matching between BBUs and UEs, and (3) matching between mobile clones (MC) and UEs. By applying the Gale-Shapley Matching Theory in the duplex matching framework, we propose a multi-stage heuristic to minimize the refusal rate for user's task offloading requests. Trace-based simulation confirms that our solution can successfully achieve near-optimal performance in such a hybrid deployment

Optimization of CNN through Novel Training Strategy for Visual Classification Problems

The convolution neural network (CNN) has achieved state-of-the-art performance in many computer vision applications e.g., classification, recognition, detection, etc. However, the global optimization of CNN training is still a problem. Fast classification and training play a key role in the development of the CNN. We hypothesize that the smoother and optimized the training of a CNN goes, the more efficient the end result becomes. Therefore, in this paper, we implement a modified resilient backpropagation (MRPROP) algorithm to improve the convergence and efficiency of CNN training. Particularly, a tolerant band is introduced to avoid network overtraining, which is incorporated with the global best concept for weight updating criteria to allow the training algorithm of the CNN to optimize its weights more swiftly and precisely. For comparison, we present and analyze four different training algorithms for CNN along with MRPROP, i.e., resilient backpropagation (RPROP), Levenberg–Marquardt (LM), conjugate gradient (CG), and gradient descent with momentum (GDM). Experimental results showcase the merit of the proposed approach on a public face and skin dataset