Doctor of Philosophy

dissertationMetamaterials have gained significant attention over the last decade because they can exhibit electromagnetic properties that are not readily available in naturally occurring materials. This dissertation describes our work on design, fabrication and characterization of liquid metal-based metamaterials with focus on their applications in the terahertz (THz) frequency range. In contrast to the more conventional approaches to fabricating these structures, which rely on vacuum deposited solid metal films, we used metals that are liquid at room temperature. This family of materials is especially attractive for such applications, since it enables large-scale reconfigurability in the overall geometry of the device. We demonstrate a number of unique plasmonic and metamaterial devices. Within the topic of plasmonics, we demonstrate a device that allows for mechanical stretching that is reversibly deformable. In an analogous structure, we can change the geometry dramatically by injecting or withdrawing liquid metals from specific area of the pattern. We also developed a liquid metal-based reconfigurable THz metamaterial device that is not only pressure driven, but also exhibits pressure memory. As an alternate approach to demonstrate reconfigurability, we developed a technique for creating dramatic configuration changes in a device via selective erasure and refilling of metamaterial unit cells that utilizes hydrochloric acid. While the approach is successful in changing the geometry, it does not allow for fine spatial control of the pattern. Thus, we have refined the approach by developing an electrolytic process to change the geometry of a liquid metal-based structured device in a more localized and controlled manner. Since liquid metals can be solidified under certain conditions, we have demonstrated a novel technique for fabrication of free-standing two-dimensional and three-dimensional terahertz metamaterial devices using injection molding of gallium. Finally, we developed a technique of printing three-dimensional solid metal structures by pulling liquid gallium out of a reservoir via solid/liquid interface. Based on these results, we are currently extending our work towards development of metamaterials that can be used in real-world applications. Based on the significant progress made the THz field over the last two decades, the likelihood of THz systems level applications is much brighter

Mechanically stretchable and reversibly deformable liquid metal-based plasmonics

pre-printWe demonstrate that liquid metals are attractive materials for active plasmonic devices at terahertz frequencies. Using a liquid metal injected into an elastomeric mold, we measure the static and stretched transmission properties of aperture arrays

Memory bites and games - environmental and mental elements affect brain health for elderly

This bachelor’s thesis is part of the Research and Development (R&D) project “Memory Bites and Games” at Lahti University of Applied Sciences. The aim of that project is to develop a new social game application which can be web-based or/and mobile based.  The function of the application is helping to train and maintain brain health, to identify early signs of cognitive malfunction or impairment.    The purpose: This literature review is to find out what are the mental, environmental elements which have affected on the development of brain diseases and how these aspects should be taken into account in preventing them. The method: Data was collected through systematic database search. The study consisted of international studies and articles were selected by peer reviewed. A qualitative literature review was used as the research method, inductive content analysis was used for data analysis. The findings: From environmental perspective, there are six factors containing lifestyle, residency, social networks, games, radiation and pollution were found to affect elder adult’s brain health. From mental perspective, five factors including lifestyle, social status, medical intervention, disease and art were found to dedicate to the quality of brain health of elderly people. In conclusion: The literature review is involved in environmental and mental elements which effect brain health of elderly people, the follow-up study about physical and cognitive elements effecting brain health is recommended in future.Tämä opinnäytetyö on osa tutkimus ja kehitys projektia " Muistin Puraisut ja Pelit " Lahden ammattikorkeakoulussa. Kyseisen projektin tavoitteena on kehittää uusi sosiaalinen pelisovellus joka voi olla selain ja/tai kännykkä pohjainen sovellus. Ohjelman tarkoituksena on auttaa harjoittelemaan, ylläpitämänä aivojen kuntoa ja tunnistamaan varhaisia merkkejä kognitiivisestä heikentymisestä tai vajaatoiminnasta. Tarkoitus: Tämä kirjallisuuskatsaus selvittää mitkä ovat psyykkisiä ja ympäristöllisiä tekijöitä, jotka vaikuttavat aivosairauksien kehittymiseen ja mitenkä nämä pitäisi huomioida niiden ehkäisemiseksi. Menetelmä: Tiedot kerättiin systemaattisesti etsimällä tietokannasta. Tutkimus sisälti kansainvälisiä tutkimuksia ja artikkeleita, jotka valittiin Melinda, Masto-finna, Academic search elite (EBSCO), Medic ja PubMed tietokannoista. Kuvaistavaa kirjallisuuskatsomusta käytettiin aineiston lähteenä ja määrällistä sisällön analyysia käytettiin analysoinnissa. Havainnot: Ympäristöllisesti on kuusi tekijää. Elämäntapa, asuinpaikka, sosiaaliset verkostot, pelit, säteily ja saaste havaittiin vaikuttavan vanhuksen aivojen terveyteen. Psyykkisiä tekijöitä oli viisi tekijää. elämäntavat, sosiaalinen asema, lääkinnällinen apu, taudit ja taide havaittiin vaikuttavan vanhusten aivoterveyteen. Yhteenveto: Kirjallisuuskatsauksessa liittyvät ympäristöllisiin ja psyykkisiin tekijöihin jotka vaikuttavat vanhusten aivojen terveyteen. Jatkotutkimus fyysisestä ja kognitiiviststa tekijöistä, jotka vaikuttavat aivojen terveyteen on suositeltavaa tulevaisuudessa.        

Can technology demonstration promote rural households’ adoption of conservation tillage in China?

Under the uncertainty of conservation tillage on output, technology demonstration, as an information disclosure mechanism, is very worthy of attention for its effects on rural households’ conservation tillage adoption. This study constructs a three-stage technology adoption model to discuss the theoretical relationship between technology demonstration and rural households’ conservation tillage adoption decision, and then empirical analyzed it using a sampling rural household data from six provinces in the main grain-producing areas of China. The results show that: First, the cognition of conservation tillage is the pre-determined stage for the adoption and its intensity. Second, technology demonstration has significant positive effect on rural households’ cognition of conservation tillage, but it strongly negative related to the adoption and adoption intensity. Third, extending the technology demonstration time cannot change the rural households’ adoption decision. Fourth, the technological demonstration has similar effects on the conservation tillage adoption of small-scale and large-scale farmers. Fifth, increasing land size helps rural households to adopt conservation tillage, while land fragmentation hinders their adoption

Filter Pruning For CNN With Enhanced Linear Representation Redundancy

Structured network pruning excels non-structured methods because they can take advantage of the thriving developed parallel computing techniques. In this paper, we propose a new structured pruning method. Firstly, to create more structured redundancy, we present a data-driven loss function term calculated from the correlation coefficient matrix of different feature maps in the same layer, named CCM-loss. This loss term can encourage the neural network to learn stronger linear representation relations between feature maps during the training from the scratch so that more homogenous parts can be removed later in pruning. CCM-loss provides us with another universal transcendental mathematical tool besides L*-norm regularization, which concentrates on generating zeros, to generate more redundancy but for the different genres. Furthermore, we design a matching channel selection strategy based on principal components analysis to exploit the maximum potential ability of CCM-loss. In our new strategy, we mainly focus on the consistency and integrality of the information flow in the network. Instead of empirically hard-code the retain ratio for each layer, our channel selection strategy can dynamically adjust each layer's retain ratio according to the specific circumstance of a per-trained model to push the prune ratio to the limit. Notably, on the Cifar-10 dataset, our method brings 93.64% accuracy for pruned VGG-16 with only 1.40M parameters and 49.60M FLOPs, the pruned ratios for parameters and FLOPs are 90.6% and 84.2%, respectively. For ResNet-50 trained on the ImageNet dataset, our approach achieves 42.8% and 47.3% storage and computation reductions, respectively, with an accuracy of 76.23%. Our code is available at https://github.com/Bojue-Wang/CCM-LRR

Development and comparison of two computational intelligence algorithms for electrical load forecasts with multiple time scales

Electricity load forecasting provides the critical information required for power institutions and authorities to develop rational, effective, and economic dispatch plans. The load forecasting at the regional power system is important for optimal management and accommodating local renewable energy sources, which is a challenging task as the demand variations are more sensitive to local weather changes (such as temperature, humidity, precipitation, and wind speed) and consumers' activities and behaviours. The paper aims to develop a new prediction method using intelligent computational algorithms. Long Short-Term Memory (LSTM), a deep recurrent neural network, explores the long-term dependency of network memory sequence data to identify intrinsic variations in both horizontals (time series) and vertical (network depth) dimensions over a longer historical period. Support Vector Machine (SVM) is a typical learning method that has been successfully implemented to solve nonlinear regression and time series problems. This paper studies the two methods and adapts the two methods to become suitable algorithms for load prediction. The paper presents the algorithms, their applications and prediction results. The prediction performance is compared for using LSTM and SVM at ultra-short, short-term, medium-term, and long-term forecasting. The results show that LSTM has higher prediction accuracy than SVM in both ultra-short and short-term forecasts, but SVM is more capable of medium-term and long-term forecasting. Finally, the epoch time for LSTM and SVM is also calculated and compared

High-resolution load forecasting on multiple time scales using Long Short-Term Memory and Support Vector Machine

Electricity load prediction is an essential tool for power system planning, operation and manage-ment. The critical information it provides can be used by energy providers to maximise power system operation efficiency and minimise system operation costs. Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) are two suitable methods that have been successfully used for analysing time series problems. In this paper, the two algorithms are explored further for load prediction; two load prediction algorithms are developed and verified by using the half-hourly load data from the University of Warwick campus energy centre with four different prediction time horizons. The novelty lies in comparing and analysing the prediction accuracy of two intelligent algorithms with multiple time scales and in exploring better scenarios for their prediction applica-tions. High-resolution load forecasting over a long range of time is also conducted in this paper. The MAPE values for the LSTM are 2.501%, 3.577%, 25.073% and 69.947% for four prediction time horizons delineated. For the SVM, the MAPE values are 2.531%, 5.039%, 7.819% and 10.841%, respectively. It is found that both methods are suitable for shorter time horizon predictions. The results show that LSTM is more capable of ultra-short and short-term forecasting, while SVM has a higher prediction accuracy in medium-term and long-term forecasts. Further investigation is per-formed via blind tests and the test results are consistent

Evolution and reform of UK electricity market

Electricity Market is structured to fund reliable electricity supply, meet the need of consumers, ensure the affordability of end-users, and support national economic development. In recent years, to meet challenging emission target set by Government, power system in the UK has a rapid increase of integration with various-scale Renewable Energy Sources (RESs) and energy storage systems (ESSs), which pushes the electricity market reform to accommodate the changes, encourage renewable energy integration, adopt new technologies, stimulate consumers participation, and ensure the power system resilience. The paper reviews the history of UK electricity market evolution, driving factors of reform, and the trend of current electricity market reform. In history, the UK electricity wholesale market has experienced three significant reform stages, which are introducing the Electricity Pool of England & Wales (the Pool) in the 1980s, implementing the New Electricity Trading Arrangements (NETA) in the 2000s, and performing the Electricity Market Reform (EMR) in 2013. To address the new emerging challenges in decarbonising power generation, the paper explains and analyses on-going electricity market changes and the trend for future electricity market reform

Zero-shot Model Diagnosis

When it comes to deploying deep vision models, the behavior of these systems must be explicable to ensure confidence in their reliability and fairness. A common approach to evaluate deep learning models is to build a labeled test set with attributes of interest and assess how well it performs. However, creating a balanced test set (i.e., one that is uniformly sampled over all the important traits) is often time-consuming, expensive, and prone to mistakes. The question we try to address is: can we evaluate the sensitivity of deep learning models to arbitrary visual attributes without an annotated test set? This paper argues the case that Zero-shot Model Diagnosis (ZOOM) is possible without the need for a test set nor labeling. To avoid the need for test sets, our system relies on a generative model and CLIP. The key idea is enabling the user to select a set of prompts (relevant to the problem) and our system will automatically search for semantic counterfactual images (i.e., synthesized images that flip the prediction in the case of a binary classifier) using the generative model. We evaluate several visual tasks (classification, key-point detection, and segmentation) in multiple visual domains to demonstrate the viability of our methodology. Extensive experiments demonstrate that our method is capable of producing counterfactual images and offering sensitivity analysis for model diagnosis without the need for a test set.Comment: Accepted in CVPR 202