Application of Neural Network Methods in the Prediction of Stock Markets

The purpose of the research is to define practical profit which can be achieved using neural network methods as a prediction instrument. The thesis investigates the ability of neural networks to forecast future events. This capability is checked on the example of price prediction during intraday trading on stock market. The executed experiments show predictions of average 1, 2, 5 and 10 minutes’ prices based on data of one day and made by two different types of forecasting systems. These systems are based on the recurrent neural networks and back propagation neural nets. The precision of the predictions is controlled by the absolute error and the error of market direction. The economical effectiveness is estimated by a special trading system. In conclusion, the best structures of neural nets are tested with data of 31 days’ interval. The best results of the average percent of profit from one transaction (buying + selling) are 0.06668654, 0.188299453, 0.349854787 and 0.453178626, they were achieved for prediction periods 1, 2, 5 and 10 minutes. The investigation can be interesting for the investors who have access to a fast information channel with a possibility of every-minute data refreshment.

Forecasting of wind speeds and directions with artificial neural networks

In this master’s thesis, wind speeds and directions were modeled with the aim of developing suitable models for hourly, daily, weekly and monthly forecasting. Artificial Neural Networks implemented in MATLAB software were used to perform the forecasts. Three main types of artificial neural network were built, namely: Feed forward neural networks, Jordan Elman neural networks and Cascade forward neural networks. Four sub models of each of these neural networks were also built, corresponding to the four forecast horizons, for both wind speeds and directions. A single neural network topology was used for each of the forecast horizons, regardless of the model type. All the models were then trained with real data of wind speeds and directions collected over a period of two years in the municipal region of Puumala in Finland. Only 70% of the data was used for training, validation and testing of the models, while the second last 15% of the data was presented to the trained models for verification. The model outputs were then compared to the last 15% of the original data, by measuring the mean square errors and sum square errors between them. Based on the results, the feed forward networks returned the lowest generalization errors for hourly, weekly and monthly forecasts of wind speeds; Jordan Elman networks returned the lowest errors when used for forecasting of daily wind speeds. Cascade forward networks gave the lowest errors when used for forecasting daily, weekly and monthly wind directions; Jordan Elman networks returned the lowest errors when used for hourly forecasting. The errors were relatively low during training of the models, but shot up upon simulation with new inputs. In addition, a combination of hyperbolic tangent transfer functions for both hidden and output layers returned better results compared to other combinations of transfer functions. In general, wind speeds were more predictable as compared to wind directions, opening up opportunities for further research into building better models for wind direction forecasting.

Kaivoslastauskoneen hybridisointianalyysi virtuaali- ja in-loop -simuloinnin avulla

Työssä tutkitaan raskaiden työkoneiden hybridisointimitoitusta simuloimalla. Työssä esitetään simulation-in-the-loop-simulointiin perustuva järjestelmä, jolla esimerkkitapauksena oleva kaivoslastauskone työympäristöineen voidaan mallintaa mekaaniselta osaltaan monikappaledynamiikkaan perustuvalla ohjelmistolla ja hybridijärjestelmän osalta Simulinkissa. Yhdistetty simulointi mahdollistaa hybridityökoneen virtuaalimallin ohjaamisen käyttäjän toimesta reaaliajassa. Simuloinnista saadaan tuloksena mm. työsykli, jota voidaan käyttää hybridisointimitoitukseen. Hybridisointi toteutetaan kahdella erilaisella kokoonpanolla, joista analysoidaan suorituskykyä sekä polttoaineen kulutusta. Tuloksia verrataan pelkästään dieselmoottoria voimanlähteenä käyttävään lastauskoneeseen. Työssä tehty tutkimus osoittaa, että (sarja-) hybridisoinnilla voidaan saavuttaa merkittäviä etuja raskaiden työkoneiden polttoainetehokkuudessa. Dieselmoottoria voidaan ajaa sellaisessa staattisessa toimintapisteessä, jonka hyötysuhde on korkea riippumatta työkoneen kuormituksesta. Saavutettu hyöty on toteutetussa tutkimuksessa parhaimmillaan jopa 56 % vähennys polttoaineenkulutuksessa. Lisäksi tarvittava dieselin nimellisteho pienenee huomattavasti. Tutkimuksen osana esitellään myös Hardware-in-the-Loop -laitteisto, jonka avulla voidaan liittää oikea sähkömoottori ja taajuudenmuuttaja osaksi virtuaalisesti simuloitua työkonetta.

Sähköajoneuvon sähkökäytön ja mekaniikan reaaliaikaisen yhdistelmäsimulaation toteuttaminen virtuaaliympäristössä

Esitetyn tutkimuksen tavoite on selvittää yksinkertaisen sähköajoneuvosimulaation avulla sähkökäyttösimulaation ja mekaniikkasimulaation välisen ohjelmallisen rajapinnan toimivuutta ja samalla toteuttaa LUT Energian hybridilaboratorion ensimmäinen rajapintaa hyödyntävä sähkökäytön ja mekaniikan yhdistelmäsimulaatio. Tutkimus on osa Lappeenrannan teknillisen yliopiston hybridikäyttötutkimusta, jossa muun muassa virtuaalisimuloinnin ja in-loop –simulaatioiden avulla tutkitaan raskaiden työkoneiden hybridisoinnin vaikutuksia esimerkiksi niiden suorituskykyyn, energiatehokkuuteen ja käytettävyyteen. Tulokset osoittavat mainitun rajapinnan toimivuuden, mikä mahdollistaa tutkimuksen etenemisen. Lisäksi raportti selvittää virtuaalisimuloinnin keskeisiä käsitteitä (virtual prototyping, hardware-in-the-loop sekä human-in-the-loop –simulointi).

Virtual technology and haptic interface solutions for design and control of mobile working machines

Commercially available haptic interfaces are usable for many purposes. However, as generic devices they are not the most suitable for the control of heavy duty mobile working machines like mining machines, container handling equipment and excavators. Alternative mechanical constructions for a haptic controller are presented and analysed. A virtual reality environment (VRE) was built to test the proposed haptic controller mechanisms. Verification of an electric motor emulating a hydraulic pump in the electro-hydraulic system of a mobile working machine is carried out. A real-time simulator using multi-body-dynamics based software with hardware-in-loop (HIL) setup was used for the tests. Recommendations for further development of a haptic controller and emulator electric motor are given.

Development of Generic Simulation Model for Mobile Working Machines

This master’s thesis has been done for Drive! –project in which a new electric motor solution for mobile working machines is developed. Generic simulation model will be used as marketing and development tool. It can be used to model a wide variety of different vehicles with and without electric motor and to show customer the difference between traditionally build vehicles and those with new electric motor solution. Customers can also use simulation model to research different solutions for their own vehicles. At the start of the project it was decided that MeVEA software would be used as main simulation program and Simulink will only be used to simulate the operation of electrical components. Development of the generic model started with the research of these two software applications, simulation models which are made with them and how these simulation models can be build faster. Best results were used for building of generic simulation model. Finished generic model can be used to produce new tractor models for real-time simulations in short notice. All information about model is collected to one datasheet which can be easily filled by the user. After datasheet is filled a script will automatically build new simulation model in seconds. At the moment generic model is capable of building simulation models for wide variety of different tractors but it can be easily altered for other vehicle types too which would also benefit greatly from electric drive solution. Those could be for example wheel loaders and harvesters.

Novel virtual environment and real-time simulation based methods for improving life-cycle efficiency of non-road mobile machinery

Virtual environments and real-time simulators (VERS) are becoming more and more important tools in research and development (R&D) process of non-road mobile machinery (NRMM). The virtual prototyping techniques enable faster and more cost-efficient development of machines compared to use of real life prototypes. High energy efficiency has become an important topic in the world of NRMM because of environmental and economic demands. The objective of this thesis is to develop VERS based methods for research and development of NRMM. A process using VERS for assessing effects of human operators on the life-cycle efficiency of NRMM was developed. Human in the loop simulations are ran using an underground mining loader to study the developed process. The simulations were ran in the virtual environment of the Laboratory of Intelligent Machines of Lappeenranta University of Technology. A physically adequate real-time simulation model of NRMM was shown to be reliable and cost effective in testing of hardware components by the means of hardware-in-the-loop (HIL) simulations. A control interface connecting integrated electro-hydraulic energy converter (IEHEC) with virtual simulation model of log crane was developed. IEHEC consists of a hydraulic pump-motor and an integrated electrical permanent magnet synchronous motorgenerator. The results show that state of the art real-time NRMM simulators are capable to solve factors related to energy consumption and productivity of the NRMM. A significant variation between the test drivers is found. The results show that VERS can be used for assessing human effects on the life-cycle efficiency of NRMM. HIL simulation responses compared to that achieved with conventional simulation method demonstrate the advances and drawbacks of various possible interfaces between the simulator and hardware part of the system under study. Novel ideas for arranging the interface are successfully tested and compared with the more traditional one. The proposed process for assessing the effects of operators on the life-cycle efficiency will be applied for wider group of operators in the future. Driving styles of the operators can be analysed statistically from sufficient large result data. The statistical analysis can find the most life-cycle efficient driving style for the specific environment and machinery. The proposed control interface for HIL simulation need to be further studied. The robustness and the adaptation of the interface in different situations must be verified. The future work will also include studying the suitability of the IEHEC for different working machines using the proposed HIL simulation method.

Experimentation on Wind Powered Hydraulic Heating System

It is common knowledge of the world’s dependency on fossil fuel for energy, its unsustainability on the long run and the changing trend towards renewable energy as an alternative energy source. This aims to cut down greenhouse gas emission and its impact on the rate of ecological and climatic change. Quite remarkably, wind energy has been one of many focus areas of renewable energy sources and has attracted lots of investment and technological advancement. The objective of this research is to explore wind energy and its application in household heating. This research aims at applying experimental approach in real time to study and verify a virtually simulated wind powered hydraulic house heating system. The hardware components comprise of an integrated hydraulic pump, flow control valve, hydraulic fluid and other hydraulic components. The system design and control applies hardware in-the-loop (HIL) simulation setup. Output signal from the semi-empirical turbine modelling controls the integrated motor to generate flow. Throttling the volume flow creates pressure drop across the valve and subsequently thermal power in the system to be outputted using a heat exchanger. Maximum thermal power is achieved by regulating valve orifice to achieve optimum system parameter. Savonius rotor is preferred for its low inertia, high starting torque and ease of design and maintenance characteristics, but lags in power efficiency. A prototype turbine design is used; with power output in range of practical Savonius turbine. The physical mechanism of the prototype turbine’s augmentation design is not known and will not be a focus in this study.

Hajautetun automaation käyttö osana simulaatiota

Työssä perehdytään simulaatiotekniikkaan, jossa käytetään osana fyysistä laitteistoa, ja siihen tarvittaviin komponentteihin, kuten ohjelmistorajapintoihin sekä kenttäväylään. Työssä tutustutaan myös IEC 61131-3 ja IEC 61499 -standardien mukaisiin toimintolohkomalleihin, joita käytetään automaatiossa. Näiden perusteella kehitetään järjestelmä, jonka avulla Simulink-ympäristössä voidaan oman toimintolohkon avulla käyttää taajuusmuuttajaa osana simulaatiota. Tällaisen reaaliaikaisen systeemin eri osien väliseen synkronointiin kiinnitetään myös huomiota. Järjestelmää testataan simulaatiomallilla, jossa syötetään vääntömomenttiohje taajuusmuuttajalle, joka mittaa siihen kytketyn moottorin pyörimisnopeuden. Mallilla voidaan esimerkiksi arvioida kuorman hitausmomentti. Työssä tutustutaan myös taajuusmuuttajien ohjelmallisiin ominaisuuksiin ja niiden perusteella pohditaan esitetyn kaltaisten järjestelmien käyttöä hajautettuna automaatiojärjestelmänä. Kokeellinen työ tehtiin säätö- ja digitaalitekniikan laboratoriossa vuoden 2010 aikana.

Use of neural networks in robot positioning of large flexible redundant manipulators

Deflection compensation of flexible boom structures in robot positioning is usually done using tables containing the magnitude of the deflection with inverse kinematics solutions of a rigid structure. The number of table values increases greatly if the working area of the boom is large and the required positioning accuracy is high. The inverse kinematics problems are very nonlinear, and if the structure is redundant, in some cases it cannot be solved in a closed form. If the structural flexibility of the manipulator arms is taken into account, the problem is almost impossible to solve using analytical methods. Neural networks offer a possibility to approximate any linear or nonlinear function. This study presents four different methods of using neural networks in the static deflection compensation and inverse kinematics solution of a flexible hydraulically driven manipulator. The training information required for training neural networks is obtained by employing a simulation model that includes elasticity characteristics. The functionality of the presented methods is tested based on the simulated and measured results of positioning accuracy. The simulated positioning accuracy is tested in 25 separate coordinate points. For each point, the positioning is tested with five different mass loads. The mean positioning error of a manipulator decreased from 31.9 mm to 4.1 mm in the test points. This accuracy enables the use of flexible manipulators in the positioning of larger objects. The measured positioning accuracy is tested in 9 separate points using three different mass loads. The mean positioning error decreased from 10.6 mm to 4.7 mm and the maximum error from 27.5 mm to 11.0 mm.

Training executive functions in the laboratory and in real life : cognitive consequences of computer-based exercises and bilingualism

The question of the trainability of executive functions and the impact of such training on related cognitive skills has stirred considerable research interest. Despite a number of studies investigating this, the question has not yet been solved. The general aim of this thesis was to investigate two very different types of training of executive functions: laboratory-based computerized training (Studies I-III) and realworld training through bilingualism (Studies IV-V). Bilingualism as a kind of training of executive functions is based on the idea that managing two languages requires executive resources, and previous studies have suggested a bilingual advantage in executive functions. Three executive functions were studied in the present thesis: updating of working memory (WM) contents, inhibition of irrelevant information, and shifting between tasks and mental sets. Studies I-III investigated the effects of computer-based training of WM updating (Study I), inhibition (Study II), and set shifting (Study III) in healthy young adults. All studies showed increased performance on the trained task. More importantly, improvement on an untrained task tapping the trained executive function (near transfer) was seen in Study I and II. None of the three studies showed improvement on untrained tasks tapping some other cognitive function (far transfer) as a result of training. Study I also used PET to investigate the effects of WM updating training on a neurotransmitter closely linked to WM, namely dopamine. The PET results revealed increased striatal dopamine release during WM updating performance as a result of training. Study IV investigated the ability to inhibit task-irrelevant stimuli in bilinguals and monolinguals by using a dichotic listening task. The results showed that the bilinguals exceeded the monolinguals in inhibiting task-irrelevant information. Study V introduced a new, complementary research approach to study the bilingual executive advantage and its underlying mechanisms. To circumvent the methodological problems related to natural groups design, this approach focuses only on bilinguals and examines whether individual differences in bilingual behavior correlate with executive task performances. Using measures that tap the three above-entioned executive functions, the results suggested that more frequent language switching was associated with better set shifting skills, and earlier acquisition of the second language was related to better inhibition skills. In conclusion, the present behavioral results showed that computer-based training of executive functions can improve performance on the trained task and on closely related tasks, but does not yield a more general improvement of cognitive skills. Moreover, the functional neuroimaging results reveal that WM training modulates striatal dopaminergic function, speaking for training-induced neural plasticity in this important neurotransmitter system. With regard to bilingualism, the results provide further support to the idea that bilingualism can enhance executive functions. In addition, the new complementary research approach proposed here provides some clues as to which aspects of everyday bilingual behavior may be related to the advantage in executive functions in bilingual individuals.

Pretraining Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks (CNN) have become the state-of-the-art methods on many large scale visual recognition tasks. For a lot of practical applications, CNN architectures have a restrictive requirement: A huge amount of labeled data are needed for training. The idea of generative pretraining is to obtain initial weights of the network by training the network in a completely unsupervised way and then fine-tune the weights for the task at hand using supervised learning. In this thesis, a general introduction to Deep Neural Networks and algorithms are given and these methods are applied to classification tasks of handwritten digits and natural images for developing unsupervised feature learning. The goal of this thesis is to find out if the effect of pretraining is damped by recent practical advances in optimization and regularization of CNN. The experimental results show that pretraining is still a substantial regularizer, however, not a necessary step in training Convolutional Neural Networks with rectified activations. On handwritten digits, the proposed pretraining model achieved a classification accuracy comparable to the state-of-the-art methods.

Modelling non-industrial private forest landowners' strategic decision making by using logistic regression and neural networks: case of predicting the choice of forest taxation basis

Summary

Business competence and training needs in Finnish companies operating in the Leningrad Region

Tässä pro gradu -työssä tutkitaan Leningradin alueella, Venäjällä, toimivien suomalaisyritysten liiketoimintaosaamisen koulutustarpeita. Tavoitteena on ollut tutkia, millaisia yritysten koulutustarpeet ovat, sekä lisäksi selvittää yleisemmällä tasolla, miten liiketoimintaosaaminen määritellään. Useat tutkimusta varten haastatellut johtajat pitävät liiketoimintaosaamista erityisesti markkinoilla toimimiseen liittyvänä osaamisena. Myös johtaminen, sekä tuotteet ja teknologia nähdään liiketoimintaosaamisen tärkeinä osina. Yrityksillä on koulutustarpeita seuraavilla alueilla: johtaminen; myynti, markkinat ja asiakkaat; yrityksen sisäinen yhteistyö; kielet, sekä juridiikka ja laskentatoimi. Haastateltavien mukaan markkinoiden nopea kehitys sekä yrityksen kasvu luovat yrityksille koulutustarpeita. Yllättäen myös Venäjän koulutusjärjestelmää itsessään pidetään koulutustarpeiden syynä. Tutkimuksessa mukana olleiden yritysten koulutuskäytännöt ovat keskenään melko erilaisia: koulutusbudjetti, koulutuspäivien määrä ja koulutusorganisaation valintakriteerit vaihtelevatyrityksestä riippuen. Joka tapauksessa yleisin koulutusmuoto näyttää olevan yrityksen sisäinen koulutus. Monet haastateltavat painottavat suuresti uusien työntekijöiden kouluttamista. Selvästikin rekrytointi ja uusien työntekijöiden koulutus vievät suuren osan tutkimusta varten haastateltujen johtajien ajasta. Tärkeä huomio koulutusmarkkinoihin liittyen on se, että lyhyiden, kaikille avoimien koulutusten kohdalla markkinat ovat Pietarissa täynnä. Suurimpana uhkana nähdään alalla vallitseva kouluttajapula.

Strategic supply lead time management. Case Nokia Networks, Hardware Services

Teollisuuden palveluiden on huomattu olevan potentiaalinen lisätulojen lähde. Teollisuuden palveluiden dynaamisessa maailmassa räätälöinti ja kyky toimia nopeasti ovat kriittisiä asiakastyytyväisyyden ja kilpailuedun luomisprosessin osia. Toimitusketjussa käytetyn ajan lyhentämisellä voidaan saavuttaa sekä paremmat vasteajat, että alhaisemmat kokonaiskustannukset. Tutkielman tavoitteena on kuvata teollisuuden palveluiden dynaamista ympäristöä: asiakastarvetta, sekä mahdollisuuksia kaventaa pyydetyn ja saavutetun toimitusajan välistä eroa. Tämä toteutetaan pääosin strategisen toimitusajan hallinnan keinoin. Langattomien tietoliikenneverkkojen operaattorit haluavat vähentää ydinosaamiseensa kuulumatomiin toimintoihin, kuten ylläpitoon sitoutuneita pääomia. Tutkielman case osiossa varaosapalvelujen toimitusketjun kysyntä-, materiaali- ja informaatiovirtoja analysoidaan niin kvalitatiivisten haastatteluiden, sisäisten dokumenttien, kuin kvantitatiivisten tilastollisten menetelmienkin avulla. Löydöksiä peilataan vallitsevaa toimitusketjun ja ajanhallinnan paradigmaa vasten. Tulokset osoittavat, että vahvan palvelukulttuurin omaksuminen ja kokonaisvaltainen toimitusketjun tehokkuuden mittaaminen ovat ajanhallinnan lähtökohtia teollisuuden palveluissa.