Piezo force microscopy and peakforce tuna measurements of III-V semiconductor nanowires

GaN, InP and GaAs nanowires were investigated for piezoelectric response. Nanowires and structures based on them can find wide applications in areas purposes such as nanogenarators, nanodrives, Solar cells and other perspective areas. Experemental measurements were carried out on AFM Bruker multimode 8 and data was handled with Nanoscope software. AFM techniques permitted not only to visualize the surface topography, but also to show distribution of piezoresponse and allowed to calculate its properties. The calculated values are in the same range as published by other authors.

Dynamic Workload Parallelization and System Calibration on Single-Chip Cloud Computer

Many-core systems are emerging from the need of more computational power and power efficiency. However there are many issues which still revolve around the many-core systems. These systems need specialized software before they can be fully utilized and the hardware itself may differ from the conventional computational systems. To gain efficiency from many-core system, programs need to be parallelized. In many-core systems the cores are small and less powerful than cores used in traditional computing, so running a conventional program is not an efficient option. Also in Network-on-Chip based processors the network might get congested and the cores might work at different speeds. In this thesis is, a dynamic load balancing method is proposed and tested on Intel 48-core Single-Chip Cloud Computer by parallelizing a fault simulator. The maximum speedup is difficult to obtain due to severe bottlenecks in the system. In order to exploit all the available parallelism of the Single-Chip Cloud Computer, a runtime approach capable of dynamically balancing the load during the fault simulation process is used. The proposed dynamic fault simulation approach on the Single-Chip Cloud Computer shows up to 45X speedup compared to a serial fault simulation approach. Many-core systems can draw enormous amounts of power, and if this power is not controlled properly, the system might get damaged. One way to manage power is to set power budget for the system. But if this power is drawn by just few cores of the many, these few cores get extremely hot and might get damaged. Due to increase in power density multiple thermal sensors are deployed on the chip area to provide realtime temperature feedback for thermal management techniques. Thermal sensor accuracy is extremely prone to intra-die process variation and aging phenomena. These factors lead to a situation where thermal sensor values drift from the nominal values. This necessitates efficient calibration techniques to be applied before the sensor values are used. In addition, in modern many-core systems cores have support for dynamic voltage and frequency scaling. Thermal sensors located on cores are sensitive to the core's current voltage level, meaning that dedicated calibration is needed for each voltage level. In this thesis a general-purpose software-based auto-calibration approach is also proposed for thermal sensors to calibrate thermal sensors on different range of voltages.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Dynamic model development of adiabatic compressed air energy storage

The share of variable renewable energy in electricity generation has seen exponential growth during the recent decades, and due to the heightened pursuit of environmental targets, the trend is to continue with increased pace. The two most important resources, wind and insolation both bear the burden of intermittency, creating a need for regulation and posing a threat to grid stability. One possibility to deal with the imbalance between demand and generation is to store electricity temporarily, which was addressed in this thesis by implementing a dynamic model of adiabatic compressed air energy storage (CAES) with Apros dynamic simulation software. Based on literature review, the existing models due to their simplifications were found insufficient for studying transient situations, and despite of its importance, the investigation of part load operation has not yet been possible with satisfactory precision. As a key result of the thesis, the cycle efficiency at design point was simulated to be 58.7%, which correlated well with literature information, and was validated through analytical calculations. The performance at part load was validated against models shown in literature, showing good correlation. By introducing wind resource and electricity demand data to the model, grid operation of CAES was studied. In order to enable the dynamic operation, start-up and shutdown sequences were approximated in dynamic environment, as far as is known, the first time, and a user component for compressor variable guide vanes (VGV) was implemented. Even in the current state, the modularly designed model offers a framework for numerous studies. The validity of the model is limited by the accuracy of VGV correlations at part load, and in addition the implementation of heat losses to the thermal energy storage is necessary to enable longer simulations. More extended use of forecasts is one of the important targets of development, if the system operation is to be optimised in future.

Measuring and improving production efficiency of paint filling and packing line

Tutkittu yritys on suomalainen maaleja ja lakkoja kansainvälisesti valmistava ja myyvä toimija. Yrityksessä otettiin vuonna 2010 käyttöön uudet tuotannon ja toimitusketjun tavoitteet ja suunnitelmat ja tämä tutkimus on osa tuota kokonaisvaltaista kehittämissuuntaa. Tutkimuksessa käsitellään tuotannon ja kunnossapidon tehokkuuden parantamis- ja mittaustyökalu OEE:tä ja tuotevaihtoaikojen pienentämiseen tarkoitettua SMED -työkalua. Työn teoriaosuus perustuu lähinnä akateemisiin julkaisuihin, mutta myös haastatteluihin, kirjoihin, internet sivuihin ja yhteen vuosikertomukseen. Empiriaosuudessa OEE:n käyttöönoton ongelmia ja onnistumista tutkittiin toistettavalla käyttäjäkyselyllä. OEE:n potentiaalia ja käyttöönottoa tutkittiin myös tarkastelemalla tuotanto- ja käytettävyysdataa, jota oli kerätty tuotantolinjalta. SMED:iä tutkittiin siihen perustuvan tietokoneohjelman avulla. SMED:iä tutkittiin teoreettisella tasolla, eikä sitä implementoitu vielä käytäntöön. Tutkimustuloksien mukaan OEE ja SMED sopivat hyvin esimerkkiyritykselle ja niissä on paljon potentiaalia. OEE ei ainoastaan paljasta käytettävyyshäviöiden määrää, mutta myös niiden rakenteen. OEE -tulosten avulla yritys voi suunnata rajalliset tuotannon ja kunnossapidon parantamisen resurssit oikeisiin paikkoihin. Työssä käsiteltävä tuotantolinja ei tuottanut mitään 56 % kaikesta suunnitellusta tuotantoajasta huhtikuussa 2016. Linjan pysähdyksistä ajallisesti 44 % johtui vaihto-, aloitus- tai lopetustöistä. Tuloksista voidaan päätellä, että käytettävyyshäviöt ovat vakava ongelma yrityksen tuotannontehokkuudessa ja vaihtotöiden vähentäminen on tärkeä kehityskohde. Vaihtoaikaa voitaisiin vähentää ~15 % yksinkertaisilla ja halvoilla SMED:illä löydetyillä muutoksilla työjärjestyksessä ja työkaluissa. Parannus olisi vielä suurempi kattavimmilla muutoksilla. SMED:in suurin potentiaali ei välttämättä ole vaihtoaikojen lyhentämisessä vaan niiden standardisoinnissa.