Design and development of auxiliary components for a new two-stroke, stratified-charge, lean-burn gasoline engine

A unique stepped-piston engine was developed by a group of research engineers at Universiti Teknologi Malaysia (UTM), from 2003 to 2005. The development work undertaken by them engulfs design, prototyping and evaluation over a predetermined period of time which was iterative and challenging in nature. The main objective of the program is to demonstrate local R&D capabilities on small engine work that is able to produce mobile powerhouse of comparable output, having low-fuel consumption and acceptable emission than its crankcase counterpart of similar displacement. A two-stroke engine work was selected as it posses a number of technological challenges, increase in its thermal efficiency, which upon successful undertakings will be useful in assisting the group in future powertrain undertakings in UTM. In its carbureted version, the single-cylinder aircooled engine incorporates a three-port transfer system and a dedicated crankcase breather. These features will enable the prototype to have high induction efficiency and to behave very much a two-stroke engine but equipped with a four-stroke crankcase lubrication system. After a series of analytical work the engine was subjected to a series of laboratory trials. It was also tested on a small watercraft platform with promising indication of its flexibility of use as a prime mover in mobile platform. In an effort to further enhance its technology features, the researchers have also embarked on the development of an add-on auxiliary system. The system comprises of an engine control unit (ECU), a directinjector unit, a dedicated lubricant dispenser unit and an embedded common rail fuel unit. This support system was incorporated onto the engine to demonstrate the finer points of environmental-friendly and fuel economy features. The outcome of this complete package is described in the report, covering the methodology and the final characteristics of the mobile power plant

Design Pattern Instances within Model Driven Development Based on Abstraction, Concretization and Variability

The main goal of the paper is to present the method of design pattern support based on principles of model driven development: the abstraction, semantics and model transformations. More specifically, the method is based on the principle of suggestion of design pattern instances via the semantic marking of model elements or source code fragments and on the subsequent transformations of this way marked models or source code. Thanks to the continual support of the design patterns at more levels of abstraction and thanks to the transformations between particular model levels and source code, the method is targeted to achieve the applicability in the area of the iterative, incremental and model driven development

Language Acquisition in Computers

This project explores the nature of language acquisition in computers, guided by techniques similar to those used in children. While existing natural language processing methods are limited in scope and understanding, our system aims to gain an understanding of language from first principles and hence minimal initial input. The first portion of our system was implemented in Java and is focused on understanding the morphology of language using bigrams. We use frequency distributions and differences between them to define and distinguish languages. English and French texts were analyzed to determine a difference threshold of 55 before the texts are considered to be in different languages, and this threshold was verified using Spanish texts. The second portion of our system focuses on gaining an understanding of the syntax of a language using a recursive method. The program uses one of two possible methods to analyze given sentences based on either sentence patterns or surrounding words. Both methods have been implemented in C++. The program is able to understand the structure of simple sentences and learn new words. In addition, we have provided some suggestions regarding future work and potential extensions of the existing program.Comment: 39 pages, 10 figures and 6 table

Chapter BCI Integration: Application Interfaces

Natural disaster

Comparing CNN and Human Crafted Features for Human Activity Recognition

Deep learning techniques such as Convolutional Neural Networks (CNNs) have shown good results in activity recognition. One of the advantages of using these methods resides in their ability to generate features automatically. This ability greatly simplifies the task of feature extraction that usually requires domain specific knowledge, especially when using big data where data driven approaches can lead to anti-patterns. Despite the advantage of this approach, very little work has been undertaken on analyzing the quality of extracted features, and more specifically on how model architecture and parameters affect the ability of those features to separate activity classes in the final feature space. This work focuses on identifying the optimal parameters for recognition of simple activities applying this approach on both signals from inertial and audio sensors. The paper provides the following contributions: (i) a comparison of automatically extracted CNN features with gold standard Human Crafted Features (HCF) is given, (ii) a comprehensive analysis on how architecture and model parameters affect separation of target classes in the feature space. Results are evaluated using publicly available datasets. In particular, we achieved a 93.38% F-Score on the UCI-HAR dataset, using 1D CNNs with 3 convolutional layers and 32 kernel size, and a 90.5% F-Score on the DCASE 2017 development dataset, simplified for three classes (indoor, outdoor and vehicle), using 2D CNNs with 2 convolutional layers and a 2x2 kernel size

GraPE: fast and scalable Graph Processing and Embedding

Graph Representation Learning methods have enabled a wide range of learning problems to be addressed for data that can be represented in graph form. Nevertheless, several real world problems in economy, biology, medicine and other fields raised relevant scaling problems with existing methods and their software implementation, due to the size of real world graphs characterized by millions of nodes and billions of edges. We present GraPE, a software resource for graph processing and random walk based embedding, that can scale with large and high-degree graphs and significantly speed up-computation. GraPE comprises specialized data structures, algorithms, and a fast parallel implementation that displays everal orders of magnitude improvement in empirical space and time complexity compared to state of the art software resources, with a corresponding boost in the performance of machine learning methods for edge and node label prediction and for the unsupervised analysis of graphs.GraPE is designed to run on laptop and desktop computers, as well as on high performance computing cluster