Hybrid multi-objective optimization and hybridized self-organizing response surface method

Numerical optimization is a technique where a computer is used to explore design parameter combinations to find extremes in performance factors. In multi-objective optimization several performance factors can be optimized simultaneously. The solution to multi-objective optimization problems is not a single design, but a family of optimized designs referred to as the Pareto frontier. The Pareto frontier is a trade-off curve in the objective function space composed of solutions where performance in one objective function is traded for performance in others. A Multi-Objective Hybridized Optimizer (MOHO) was created for the purpose of solving multi-objective optimization problems by utilizing a set of constituent optimization algorithms. MOHO tracks the progress of the Pareto frontier approximation development and automatically switches amongst those constituent evolutionary optimization algorithms to speed the formation of an accurate Pareto frontier approximation. Aerodynamic shape optimization is one of the oldest applications of numerical optimization. MOHO was used to perform shape optimization on a 0.5-inch ballistic penetrator traveling at Mach number 2.5. Two objectives were simultaneously optimized: minimize aerodynamic drag and maximize penetrator volume. This problem was solved twice. The first time the problem was solved by using Modified Newton Impact Theory (MNIT) to determine the pressure drag on the penetrator. In the second solution, a Parabolized Navier-Stokes (PNS) solver that includes viscosity was used to evaluate the drag on the penetrator. The studies show the difference in the optimized penetrator shapes when viscosity is absent and present in the optimization. In modern optimization problems, objective function evaluations may require many hours on a computer cluster to perform these types of analysis. One solution is to create a response surface that models the behavior of the objective function. Once enough data about the behavior of the objective function has been collected, a response surface can be used to represent the actual objective function in the optimization process. The Hybrid Self-Organizing Response Surface Method (HYBSORSM) algorithm was developed and used to make response surfaces of objective functions. HYBSORSM was evaluated using a suite of 295 non-linear functions. These functions involve from 2 to 100 variables demonstrating robustness and accuracy of HYBSORSM.

Evaluation of wind-induced internal pressure in low-rise buildings: A multi scale experimental and numerical approach

Hurricane is one of the most destructive and costly natural hazard to the built environment and its impact on low-rise buildings, particularity, is beyond acceptable. The major objective of this research was to perform a parametric evaluation of internal pressure (IP) for wind-resistant design of low-rise buildings and wind-driven natural ventilation applications. For this purpose, a multi-scale experimental, i.e. full-scale at Wall of Wind (WoW) and small-scale at Boundary Layer Wind Tunnel (BLWT), and a Computational Fluid Dynamics (CFD) approach was adopted. This provided new capability to assess wind pressures realistically on internal volumes ranging from small spaces formed between roof tiles and its deck to attic to room partitions. Effects of sudden breaching, existing dominant openings on building envelopes as well as compartmentalization of building interior on the IP were systematically investigated. Results of this research indicated: (i) for sudden breaching of dominant openings, the transient overshooting response was lower than the subsequent steady state peak IP and internal volume correction for low-wind-speed testing facilities was necessary. For example a building without volume correction experienced a response four times faster and exhibited 30–40% lower mean and peak IP; (ii) for existing openings, vent openings uniformly distributed along the roof alleviated, whereas one sided openings aggravated the IP; (iii) larger dominant openings exhibited a higher IP on the building envelope, and an off-center opening on the wall exhibited (30–40%) higher IP than center located openings; (iv) compartmentalization amplified the intensity of IP and; (v) significant underneath pressure was measured for field tiles, warranting its consideration during net pressure evaluations. The study aimed at wind driven natural ventilation indicated: (i) the IP due to cross ventilation was 1.5 to 2.5 times higher for Ainlet/Aoutlet>1 compared to cases where Ainlet/Aoutlet<1, this in effect reduced the mixing of air inside the building and hence the ventilation effectiveness; (ii) the presence of multi-room partitioning increased the pressure differential and consequently the air exchange rate. Overall good agreement was found between the observed large-scale, small-scale and CFD based IP responses. Comparisons with ASCE 7-10 consistently demonstrated that the code underestimated peak positive and suction IP.

A Customer's Expectation and Perception of Hotel Service Quality in Cyprus

In recent years, hotels in Cyprus have encountered difficult economic times due to increasing customer demands and strong internal industry development competition. The hospitality industry’s main concern globally is to serve its customer S needs and desires, most of which are addressed through personal services. Hence, the hotel businesses that are able to provide quality services to its ever-demanding customers in a warm and efficient manner are those businesses which will be more likely to obtain a long term competitive advantage over their rivals. Ironically, the quality of services frequently cannot fully appreciated until something goes wrong, and then, the poor quality of services can have long lasting lingering effects on the customer base and, hence, often is translated into a loss of business. Nevertheless, since the issue of delivery of hospitality services always involves people, this issue must center around the management of the human resource factor, and in particular, on the way which interacts with itself and with guests, as service encounters. In the eyes of guests, hospitality businesses will be viewed successful or failure, depending on [he cumulative impact of the service encounters they have experienced on a personal level. Finally, since hotels are offering intangible and perishable personal service encounters, managing these services must be a paramount concern of any hotel business. As a preliminary exercise, visualize when you have last visited a hotel, or a restaurant, and then, ask yourself these questions: What did you feel about the quality of the experience? Was it a memorable one, which you would recommend it to others, or there were certain things, which could have made the difference? Thus, the way personalized services are provided can make the deference in attracting arid retaining long-term customers

Tool cutting force modeling and wear estimation of micro -end-milling operations

The applications of micro-end-milling operations have increased recently. A Micro-End-Milling Operation Guide and Research Tool (MOGART) package has been developed for the study and monitoring of micro-end-milling operations. It includes an analytical cutting force model, neural network based data mapping and forecasting processes, and genetic algorithms based optimization routines. MOGART uses neural networks to estimate tool machinability and forecast tool wear from the experimental cutting force data, and genetic algorithms with the analytical model to monitor tool wear, breakage, run-out, cutting conditions from the cutting force profiles. ^ The performance of MOGART has been tested on the experimental data of over 800 experimental cases and very good agreement has been observed between the theoretical and experimental results. The MOGART package has been applied to the micro-end-milling operation study of Engineering Prototype Center of Radio Technology Division of Motorola Inc. ^

A comparison of the Kodaly method and the traditional method to determine pitch accuracy in grade 6 choral sight-singing

The purpose of this study was to determine which of the two methods is more appropriate to teach pitch discrimination to Grade 6 choral students to improve sight-singing note accuracy. This study consisted of three phases: pre-testing, instruction and post-testing. During the four week study, the experimental group received training using the Kodaly method while the control group received training using the traditional method. The pre and post tests were evaluated by three trained musicians. The analysis of the data utilized an independent t-test and a paired t-test with the methods of teaching (experimental and control) as a factor. Quantitative results suggest that the experimental subjects, those receiving Kodaly instruction at post-treatment showed a significant improvement in the pitch accuracy than the control group. The specific change resulted in the Kodaly method to be more effective in producing accurate pitch in sight-singing.

Tool cutting force modeling and wear estimation of micro-end-milling operations

The applications of micro-end-milling operations have increased recently. A Micro-End-Milling Operation Guide and Research Tool (MOGART) package has been developed for the study and monitoring of micro-end-milling operations. It includes an analytical cutting force model, neural network based data mapping and forecasting processes, and genetic algorithms based optimization routines. MOGART uses neural networks to estimate tool machinability and forecast tool wear from the experimental cutting force data, and genetic algorithms with the analytical model to monitor tool wear, breakage, run-out, cutting conditions from the cutting force profiles. The performance of MOGART has been tested on the experimental data of over 800 experimental cases and very good agreement has been observed between the theoretical and experimental results. The MOGART package has been applied to the micro-end-milling operation study of Engineering Prototype Center of Radio Technology Division of Motorola Inc.

Estimation and prediction of average vehicle occupancies using traffic accident records

As congestion management strategies begin to put more emphasis on person trips than vehicle trips, the need for vehicle occupancy data has become more critical. The traditional methods of collecting these data include the roadside windshield method and the carousel method. These methods are labor-intensive and expensive. An alternative to these traditional methods is to make use of the vehicle occupancy information in traffic accident records. This method is cost effective and may provide better spatial and temporal coverage than the traditional methods. However, this method is subject to potential biases resulting from under- and over-involvement of certain population sectors and certain types of accidents in traffic accident records. In this dissertation, three such potential biases, i.e., accident severity, driver’s age, and driver’s gender, were investigated and the corresponding bias factors were developed as needed. The results show that although multi-occupant vehicles are involved in higher percentages of severe accidents than are single-occupant vehicles, multi-occupant vehicles in the whole accident vehicle population were not overrepresented in the accident database. On the other hand, a significant difference was found between the distributions of the ages and genders of drivers involved in accidents and those of the general driving population. An information system that incorporates adjustments for the potential biases was developed to estimate the average vehicle occupancies (AVOs) for different types of roadways on the Florida state roadway system. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations. In addition, comparisons of AVOs from accident data with the field estimates show that the two data sources produce relatively consistent results. While accident records can be used to obtain the historical AVO trends and field data can be used to estimate the current AVOs, no known methods have been developed to project future AVOs. Four regression models for the purpose of predicting weekday AVOs on different levels of geographic areas and roadway types were developed as part of this dissertation. The models show that such socioeconomic factors as income, vehicle ownership, and employment have a significant impact on AVOs.

Estimation and Prediction of Average Vehicle Occupancies using Traffic Accident Records

As congestion management strategies begin to put more emphasis on person trips than vehicle trips, the need for vehicle occupancy data has become more critical. The traditional methods of collecting these data include the roadside windshield method and the carousel method. These methods are labor-intensive and expensive. An alternative to these traditional methods is to make use of the vehicle occupancy information in traffic accident records. This method is cost effective and may provide better spatial and temporal coverage than the traditional methods. However, this method is subject to potential biases resulting from under- and over-involvement of certain population sectors and certain types of accidents in traffic accident records. In this dissertation, three such potential biases, i.e., accident severity, driver¡¯s age, and driver¡¯s gender, were investigated and the corresponding bias factors were developed as needed. The results show that although multi-occupant vehicles are involved in higher percentages of severe accidents than are single-occupant vehicles, multi-occupant vehicles in the whole accident vehicle population were not overrepresented in the accident database. On the other hand, a significant difference was found between the distributions of the ages and genders of drivers involved in accidents and those of the general driving population. An information system that incorporates adjustments for the potential biases was developed to estimate the average vehicle occupancies (AVOs) for different types of roadways on the Florida state roadway system. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations. In addition, comparisons of AVOs from accident data with the field estimates show that the two data sources produce relatively consistent results. While accident records can be used to obtain the historical AVO trends and field data can be used to estimate the current AVOs, no known methods have been developed to project future AVOs. Four regression models for the purpose of predicting weekday AVOs on different levels of geographic areas and roadway types were developed as part of this dissertation. The models show that such socioeconomic factors as income, vehicle ownership, and employment have a significant impact on AVOs.

High performance shift invariant motion estimation and compensation in wavelet domain video compression

The contributions of this dissertation are in the development of two new interrelated approaches to video data compression: (1) A level-refined motion estimation and subband compensation method for the effective motion estimation and motion compensation. (2) A shift-invariant sub-decimation decomposition method in order to overcome the deficiency of the decimation process in estimating motion due to its shift-invariant property of wavelet transform. ^ The enormous data generated by digital videos call for an intense need of efficient video compression techniques to conserve storage space and minimize bandwidth utilization. The main idea of video compression is to reduce the interpixel redundancies inside and between the video frames by applying motion estimation and motion compensation (MEMO) in combination with spatial transform coding. To locate the global minimum of the matching criterion function reasonably, hierarchical motion estimation by coarse to fine resolution refinements using discrete wavelet transform is applied due to its intrinsic multiresolution and scalability natures. ^ Due to the fact that most of the energies are concentrated in the low resolution subbands while decreased in the high resolution subbands, a new approach called level-refined motion estimation and subband compensation (LRSC) method is proposed. It realizes the possible intrablocks in the subbands for lower entropy coding while keeping the low computational loads of motion estimation as the level-refined method, thus to achieve both temporal compression quality and computational simplicity. ^ Since circular convolution is applied in wavelet transform to obtain the decomposed subframes without coefficient expansion, symmetric-extended wavelet transform is designed on the finite length frame signals for more accurate motion estimation without discontinuous boundary distortions. ^ Although wavelet transformed coefficients still contain spatial domain information, motion estimation in wavelet domain is not as straightforward as in spatial domain due to the shift variance property of the decimation process of the wavelet transform. A new approach called sub-decimation decomposition method is proposed, which maintains the motion consistency between the original frame and the decomposed subframes, improving as a consequence the wavelet domain video compressions by shift invariant motion estimation and compensation. ^

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Color-based surface reflectance separation for scene illumination estimation and rendering

Given the importance of color processing in computer vision and computer graphics, estimating and rendering illumination spectral reflectance of image scenes is important to advance the capability of a large class of applications such as scene reconstruction, rendering, surface segmentation, object recognition, and reflectance estimation. Consequently, this dissertation proposes effective methods for reflection components separation and rendering in single scene images. Based on the dichromatic reflectance model, a novel decomposition technique, named the Mean-Shift Decomposition (MSD) method, is introduced to separate the specular from diffuse reflectance components. This technique provides a direct access to surface shape information through diffuse shading pixel isolation. More importantly, this process does not require any local color segmentation process, which differs from the traditional methods that operate by aggregating color information along each image plane. ^ Exploiting the merits of the MSD method, a scene illumination rendering technique is designed to estimate the relative contributing specular reflectance attributes of a scene image. The image feature subset targeted provides a direct access to the surface illumination information, while a newly introduced efficient rendering method reshapes the dynamic range distribution of the specular reflectance components over each image color channel. This image enhancement technique renders the scene illumination reflection effectively without altering the scene’s surface diffuse attributes contributing to realistic rendering effects. ^ As an ancillary contribution, an effective color constancy algorithm based on the dichromatic reflectance model was also developed. This algorithm selects image highlights in order to extract the prominent surface reflectance that reproduces the exact illumination chromaticity. This evaluation is presented using a novel voting scheme technique based on histogram analysis. ^ In each of the three main contributions, empirical evaluations were performed on synthetic and real-world image scenes taken from three different color image datasets. The experimental results show over 90% accuracy in illumination estimation contributing to near real world illumination rendering effects. ^

A nested Petri net framework for modeling and analyzing multi-agent systems

In the past two decades, multi-agent systems (MAS) have emerged as a new paradigm for conceptualizing large and complex distributed software systems. A multi-agent system view provides a natural abstraction for both the structure and the behavior of modern-day software systems. Although there were many conceptual frameworks for using multi-agent systems, there was no well established and widely accepted method for modeling multi-agent systems. This dissertation research addressed the representation and analysis of multi-agent systems based on model-oriented formal methods. The objective was to provide a systematic approach for studying MAS at an early stage of system development to ensure the quality of design. ^ Given that there was no well-defined formal model directly supporting agent-oriented modeling, this study was centered on three main topics: (1) adapting a well-known formal model, predicate transition nets (PrT nets), to support MAS modeling; (2) formulating a modeling methodology to ease the construction of formal MAS models; and (3) developing a technique to support machine analysis of formal MAS models using model checking technology. PrT nets were extended to include the notions of dynamic structure, agent communication and coordination to support agent-oriented modeling. An aspect-oriented technique was developed to address the modularity of agent models and compositionality of incremental analysis. A set of translation rules were defined to systematically translate formal MAS models to concrete models that can be verified through the model checker SPIN (Simple Promela Interpreter). ^ This dissertation presents the framework developed for modeling and analyzing MAS, including a well-defined process model based on nested PrT nets, and a comprehensive methodology to guide the construction and analysis of formal MAS models.^

Leakage temperature dependency aware real-time scheduling for power and thermal optimization

Catering to society's demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. ^ In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research. ^

Real-time scheduling of embedded applications on multi-core platforms

For the past several decades, we have experienced the tremendous growth, in both scale and scope, of real-time embedded systems, thanks largely to the advances in IC technology. However, the traditional approach to get performance boost by increasing CPU frequency has been a way of past. Researchers from both industry and academia are turning their focus to multi-core architectures for continuous improvement of computing performance. In our research, we seek to develop efficient scheduling algorithms and analysis methods in the design of real-time embedded systems on multi-core platforms. Real-time systems are the ones with the response time as critical as the logical correctness of computational results. In addition, a variety of stringent constraints such as power/energy consumption, peak temperature and reliability are also imposed to these systems. Therefore, real-time scheduling plays a critical role in design of such computing systems at the system level. We started our research by addressing timing constraints for real-time applications on multi-core platforms, and developed both partitioned and semi-partitioned scheduling algorithms to schedule fixed priority, periodic, and hard real-time tasks on multi-core platforms. Then we extended our research by taking temperature constraints into consideration. We developed a closed-form solution to capture temperature dynamics for a given periodic voltage schedule on multi-core platforms, and also developed three methods to check the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research by incorporating the power/energy constraint with thermal awareness into our research problem. We investigated the energy estimation problem on multi-core platforms, and developed a computation efficient method to calculate the energy consumption for a given voltage schedule on a multi-core platform. In this dissertation, we present our research in details and demonstrate the effectiveness and efficiency of our approaches with extensive experimental results.

Development and assessment of a method to estimate meal intake of nursing home residents

The objective was to develop and validate a method for estimating food intake of nursing home residents. The study was conducted with certified nursing assistants (CNAs) at a 180-bed nursing facility. CNAs assisted in the development of the new method by providing feedback on existing estimation methods. Four simulated resident trays were used to estimate both food intake and overall meal intake. Twelve CNAs' intake estimates for 34 simulated food items (n=384 estimates) were compared to weighed values. Eightyfive percent of the 384 intake estimates for the simulated food items were correct; Cohen's kappa was 0.80, p