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AN ANALOGY BASED COSTING SYSTEM FOR INJECTION MOLDS BASED UPON GEOMETRY SIMILARITY WITH WAVELETS
Title
AN ANALOGY BASED COSTING SYSTEM FOR INJECTION MOLDS BASED UPON GEOMETRY SIMILARITY WITH WAVELETS.
Creator
Hillsman, Cyrus, Wang, Yan, University of Central Florida
Abstract / Description
The injection molding industry is large and diversified. However there is no universally accepted way to bid molds, despite the fact that the mold and related design comprise 50% of the total cost of an injection-molded part over its lifetime. This is due to both the structure of the industry and technical difficulties in developing an automated and practical cost estimation system. The technical challenges include lack of a common data format for both parts and molds; the comprehensive...
Show moreThe injection molding industry is large and diversified. However there is no universally accepted way to bid molds, despite the fact that the mold and related design comprise 50% of the total cost of an injection-molded part over its lifetime. This is due to both the structure of the industry and technical difficulties in developing an automated and practical cost estimation system. The technical challenges include lack of a common data format for both parts and molds; the comprehensive consideration of the data about a wide variety of mold types, designs, complexities, number of cavities and other factors that directly affect cost; and the robustness of estimation due to variations of build time and cost. In this research, we propose a new mold cost estimation approach based upon clustered features of parts. Geometry similarity is used to estimate the complexity of a mold from a 2D image with one orthographic view of the injection-molded part. Wavelet descriptors of boundaries as well as other inherent shape properties such as size, number of boundaries, etc. are used to describe the complexity of the part. Regression models are then built to predict costs. In addition to mean estimates, prediction intervals are calculated to support risk management.
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Date Issued
2009
Identifier
CFE0002866, ucf:48041
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002866
THE EFFECTS OF ETHNIC DIVERSITY, PERCEIVED SIMILARITY, AND TRUST ON COLLABORATIVE BEHAVIOR AND PERFORMANCE
Title
THE EFFECTS OF ETHNIC DIVERSITY, PERCEIVED SIMILARITY, AND TRUST ON COLLABORATIVE BEHAVIOR AND PERFORMANCE.
Creator
Wildman, Jessica, Salas, Eduardo, University of Central Florida
Abstract / Description
Recent issues such as global economic crises, terrorism, and conservation efforts are making international collaboration a critical topic. While cultural diversity often brings with it new perspectives and innovative solutions, diversity in collaborative settings can also lead to misunderstandings and interaction problems. Therefore, there is a pressing need to understand the processes and influences of intercultural collaboration and how to manage the collaborative process to result in the...
Show moreRecent issues such as global economic crises, terrorism, and conservation efforts are making international collaboration a critical topic. While cultural diversity often brings with it new perspectives and innovative solutions, diversity in collaborative settings can also lead to misunderstandings and interaction problems. Therefore, there is a pressing need to understand the processes and influences of intercultural collaboration and how to manage the collaborative process to result in the most effective outcomes possible. In order to address this need, the current study examines the effect of ethnic diversity, perceived deep-level similarity, trust, and distrust on collaborative behavior and performance in decision-making dyads. Participants were assigned to either same-ethnicity or different-ethnicity dyads and worked together on a political simulation game in which they had to make complex decisions to solve societal problems and increase their popularity. The results of this study indicate that ethnically similar dyads reported higher levels of perceived deep-level similarity than ethnically dissimilar dyads, and that this perceived deep-level similarity served as the mediating mechanism between objective differences in ethnic diversity and trust and distrust, respectively. The findings also suggest that trust and distrust attitudes, when considered together as a multiple mediation model, mediate the positive relationship between perceived deep-level similarity and collaborative behavior. Finally, results show that collaborative behavior significantly predicts objective performance on the political decision-making simulation. The implications of this study for theory and practice are discussed along with the study limitations and several suggestions for future research.
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Date Issued
2010
Identifier
CFE0003102, ucf:48299
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003102
Stereotypes, Perceptions of Similarity, and Cultural Identity: Factors That May Influence the Academic Achievement of Immigrant Students.
Title
Stereotypes, Perceptions of Similarity, and Cultural Identity: Factors That May Influence the Academic Achievement of Immigrant Students.
Creator
Fagan, Tamara, Szente, Judit, Eriksson, Gillian, Englehart, Deirdre, University of Central Florida
Abstract / Description
For decades, the United States has been known as the nation of immigrants due to the increasing number of immigrant students in the public school system. Although the population of immigrant students steadily increases annually, American society still pressures immigrants into acculturation to fulfill the United States ideals of academic achievement despite the United States claim of multiculturalism (Malcolm (&) Lowery, 2011). This research focuses on 1st- and 2nd generation immigrant...
Show moreFor decades, the United States has been known as the nation of immigrants due to the increasing number of immigrant students in the public school system. Although the population of immigrant students steadily increases annually, American society still pressures immigrants into acculturation to fulfill the United States ideals of academic achievement despite the United States claim of multiculturalism (Malcolm (&) Lowery, 2011). This research focuses on 1st- and 2nd generation immigrant students' strife of acceptance in U.S. culture, while sill preserving their own native culture, and the influence it has on academic achievement.The researcher interviewed eight (8) adult participants who are either 1st- or 2nd generation immigrant college students. This qualitative case study research aims to determine if forced acculturation or assimilation using stereotypes and perceptions of similarity effects how immigrant students develop their cultural identity, and the influence it has on academic achievement. Four major themes emerged from the participants' responses: parental approval, peer pressure, environmental influence, and feelings about their ethnic group. Basic findings supported that immigrant students' cultural identity is threatened by stereotypes and perceptions of similarity.
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Date Issued
2013
Identifier
CFE0004996, ucf:49554
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004996
LEARNING TECHNIQUES FOR INFORMATION RETRIEVAL AND MINING IN HIGH-DIMENSIONAL DATABASES
Title
LEARNING TECHNIQUES FOR INFORMATION RETRIEVAL AND MINING IN HIGH-DIMENSIONAL DATABASES.
Creator
Cheng, Hao, Hua, Kien A., University of Central Florida
Abstract / Description
The main focus of my research is to design effective learning techniques for information retrieval and mining in high-dimensional databases. There are two main aspects in the retrieval and mining research: accuracy and efficiency. The accuracy problem is how to return results which can better match the ground truth, and the efficiency problem is how to evaluate users' requests and execute learning algorithms as fast as possible. However, these problems are non-trivial because of the...
Show moreThe main focus of my research is to design effective learning techniques for information retrieval and mining in high-dimensional databases. There are two main aspects in the retrieval and mining research: accuracy and efficiency. The accuracy problem is how to return results which can better match the ground truth, and the efficiency problem is how to evaluate users' requests and execute learning algorithms as fast as possible. However, these problems are non-trivial because of the complexity of the high-level semantic concepts, the heterogeneous natures of the feature space, the high dimensionality of data representations and the size of the databases. My dissertation is dedicated to addressing these issues. Specifically, my work has five main contributions as follows. The first contribution is a novel manifold learning algorithm, Local and Global Structures Preserving Projection (LGSPP), which defines salient low-dimensional representations for the high-dimensional data. A small number of projection directions are sought in order to properly preserve the local and global structures for the original data. Specifically, two groups of points are extracted for each individual point in the dataset: the first group contains the nearest neighbors of the point, and the other set are a few sampled points far away from the point. These two point sets respectively characterize the local and global structures with regard to the data point. The objective of the embedding is to minimize the distances of the points in each local neighborhood and also to disperse the points far away from their respective remote points in the original space. In this way, the relationships between the data in the original space are well preserved with little distortions. The second contribution is a new constrained clustering algorithm. Conventionally, clustering is an unsupervised learning problem, which systematically partitions a dataset into a small set of clusters such that data in each cluster appear similar to each other compared with those in other clusters. In the proposal, the partial human knowledge is exploited to find better clustering results. Two kinds of constraints are integrated into the clustering algorithm. One is the must-link constraint, indicating that the involved two points belong to the same cluster. On the other hand, the cannot-link constraint denotes that two points are not within the same cluster. Given the input constraints, data points are arranged into small groups and a graph is constructed to preserve the semantic relations between these groups. The assignment procedure makes a best effort to assign each group to a feasible cluster without violating the constraints. The theoretical analysis reveals that the probability of data points being assigned to the true clusters is much higher by the new proposal, compared to conventional methods. In general, the new scheme can produce clusters which can better match the ground truth and respect the semantic relations between points inferred from the constraints. The third contribution is a unified framework for partition-based dimension reduction techniques, which allows efficient similarity retrieval in the high-dimensional data space. Recent similarity search techniques, such as Piecewise Aggregate Approximation (PAA), Segmented Means (SMEAN) and Mean-Standard deviation (MS), prove to be very effective in reducing data dimensionality by partitioning dimensions into subsets and extracting aggregate values from each dimension subset. These partition-based techniques have many advantages including very efficient multi-phased pruning while being simple to implement. They, however, are not adaptive to different characteristics of data in diverse applications. In this study, a unified framework for these partition-based techniques is proposed and the issue of dimension partitions is examined in this framework. An investigation of the relationships of query selectivity and the dimension partition schemes discovers indicators which can predict the performance of a partitioning setting. Accordingly, a greedy algorithm is designed to effectively determine a good partitioning of data dimensions so that the performance of the reduction technique is robust with regard to different datasets. The fourth contribution is an effective similarity search technique in the database of point sets. In the conventional model, an object corresponds to a single vector. In the proposed study, an object is represented by a set of points. In general, this new representation can be used in many real-world applications and carries much more local information, but the retrieval and learning problems become very challenging. The Hausdorff distance is the common distance function to measure the similarity between two point sets, however, this metric is sensitive to outliers in the data. To address this issue, a novel similarity function is defined to better capture the proximity of two objects, in which a one-to-one mapping is established between vectors of the two objects. The optimal mapping minimizes the sum of distances between each paired points. The overall distance of the optimal matching is robust and has high retrieval accuracy. The computation of the new distance function is formulated into the classical assignment problem. The lower-bounding techniques and early-stop mechanism are also proposed to significantly accelerate the expensive similarity search process. The classification problem over the point-set data is called Multiple Instance Learning (MIL) in the machine learning community in which a vector is an instance and an object is a bag of instances. The fifth contribution is to convert the MIL problem into a standard supervised learning in the conventional vector space. Specially, feature vectors of bags are grouped into clusters. Each object is then denoted as a bag of cluster labels, and common patterns of each category are discovered, each of which is further reconstructed into a bag of features. Accordingly, a bag is effectively mapped into a feature space defined by the distances from this bag to all the derived patterns. The standard supervised learning algorithms can be applied to classify objects into pre-defined categories. The results demonstrate that the proposal has better classification accuracy compared to other state-of-the-art techniques. In the future, I will continue to explore my research in large-scale data analysis algorithms, applications and system developments. Especially, I am interested in applications to analyze the massive volume of online data.
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Date Issued
2009
Identifier
CFE0002882, ucf:48022
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002882
Hashing for Multimedia Similarity Modeling and Large-Scale Retrieval
Title
Hashing for Multimedia Similarity Modeling and Large-Scale Retrieval.
Creator
Li, Kai, Hua, Kien, Qi, GuoJun, Hu, Haiyan, Wang, Chung-Ching, University of Central Florida
Abstract / Description
In recent years, the amount of multimedia data such as images, texts, and videos have been growing rapidly on the Internet. Motivated by such trends, this thesis is dedicated to exploiting hashing-based solutions to reveal multimedia data correlations and support intra-media and inter-media similarity search among huge volumes of multimedia data.We start by investigating a hashing-based solution for audio-visual similarity modeling and apply it to the audio-visual sound source localization...
Show moreIn recent years, the amount of multimedia data such as images, texts, and videos have been growing rapidly on the Internet. Motivated by such trends, this thesis is dedicated to exploiting hashing-based solutions to reveal multimedia data correlations and support intra-media and inter-media similarity search among huge volumes of multimedia data.We start by investigating a hashing-based solution for audio-visual similarity modeling and apply it to the audio-visual sound source localization problem. We show that synchronized signals in audio and visual modalities demonstrate similar temporal changing patterns in certain feature spaces. We propose to use a permutation-based random hashing technique to capture the temporal order dynamics of audio and visual features by hashing them along the temporal axis into a common Hamming space. In this way, the audio-visual correlation problem is transformed into a similarity search problem in the Hamming space. Our hashing-based audio-visual similarity modeling has shown superior performances in the localization and segmentation of sounding objects in videos.The success of the permutation-based hashing method motivates us to generalize and formally define the supervised ranking-based hashing problem, and study its application to large-scale image retrieval. Specifically, we propose an effective supervised learning procedure to learn optimized ranking-based hash functions that can be used for large-scale similarity search. Compared with the randomized version, the optimized ranking-based hash codes are much more compact and discriminative. Moreover, it can be easily extended to kernel space to discover more complex ranking structures that cannot be revealed in linear subspaces. Experiments on large image datasets demonstrate the effectiveness of the proposed method for image retrieval.We further studied the ranking-based hashing method for the cross-media similarity search problem. Specifically, we propose two optimization methods to jointly learn two groups of linear subspaces, one for each media type, so that features' ranking orders in different linear subspaces maximally preserve the cross-media similarities. Additionally, we develop this ranking-based hashing method in the cross-media context into a flexible hashing framework with a more general solution. We have demonstrated through extensive experiments on several real-world datasets that the proposed cross-media hashing method can achieve superior cross-media retrieval performances against several state-of-the-art algorithms.Lastly, to make better use of the supervisory label information, as well as to further improve the efficiency and accuracy of supervised hashing, we propose a novel multimedia discrete hashing framework that optimizes an instance-wise loss objective, as compared to the pairwise losses, using an efficient discrete optimization method. In addition, the proposed method decouples the binary codes learning and hash function learning into two separate stages, thus making the proposed method equally applicable for both single-media and cross-media search. Extensive experiments on both single-media and cross-media retrieval tasks demonstrate the effectiveness of the proposed method.
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Date Issued
2017
Identifier
CFE0006759, ucf:51840
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006759
A Psychophysical Approach to Standardizing Texture Compression for Virtual Environments
Title
A Psychophysical Approach to Standardizing Texture Compression for Virtual Environments.
Creator
Flynn, Jeremy, Szalma, James, Fidopiastis, Cali, Jentsch, Florian, Shah, Mubarak, University of Central Florida
Abstract / Description
Image compression is a technique to reduce overall data size, but its effects on human perception have not been clearly established. The purpose of this effort was to determine the most effective psychophysical method for subjective image quality assessment, and to apply those findings to an objective algorithm. This algorithm was used to identify the minimum level of texture compression noticeable to the human, in order to determine whether compression-induced texture distortion impacted...
Show moreImage compression is a technique to reduce overall data size, but its effects on human perception have not been clearly established. The purpose of this effort was to determine the most effective psychophysical method for subjective image quality assessment, and to apply those findings to an objective algorithm. This algorithm was used to identify the minimum level of texture compression noticeable to the human, in order to determine whether compression-induced texture distortion impacted game-play outcomes. Four experiments tested several hypotheses. The first hypothesis evaluated which of three magnitude estimation (ME) methods (absolute ME, absolute ME plus, or ME with a standard) for image quality assessment was the most reliable. The just noticeable difference (JND) point for textures compression against the Feature Similarity Index for color was determined The second hypothesis tested whether human participants perceived the same amount of distortion differently when textures were presented in three ways: when textures were displayed as flat images; when textures were wrapped around a model; and when textures were wrapped around models and in a virtual environment. The last set of hypotheses examined whether compression affected both subjective (immersion, technology acceptance, usability) and objective (performance) gameplay outcomes. The results were: the absolute magnitude estimation method was the most reliable; no difference was observed in the JND threshold between flat textures and textures placed on models, but textured embedded within the virtual environment were more noticeable than in the other two presentation formats. There were no differences in subjective gameplay outcomes when textures were compressed to below the JND thresholds; and those who played a game with uncompressed textures performed better on in-game tasks than those with the textures compressed, but only on the first in-game day. Practitioners and researchers can use these findings to guide their approaches to texture compression and experimental design.
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Date Issued
2018
Identifier
CFE0007178, ucf:52250
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007178
Analysis of Behaviors in Crowd Videos
Title
Analysis of Behaviors in Crowd Videos.
Creator
Mehran, Ramin, Shah, Mubarak, Sukthankar, Gita, Behal, Aman, Tappen, Marshall, Moore, Brian, University of Central Florida
Abstract / Description
In this dissertation, we address the problem of discovery and representation of group activity of humans and objects in a variety of scenarios, commonly encountered in vision applications. The overarching goal is to devise a discriminative representation of human motion in social settings, which captures a wide variety of human activities observable in video sequences. Such motion emerges from the collective behavior of individuals and their interactions and is a significant source of...
Show moreIn this dissertation, we address the problem of discovery and representation of group activity of humans and objects in a variety of scenarios, commonly encountered in vision applications. The overarching goal is to devise a discriminative representation of human motion in social settings, which captures a wide variety of human activities observable in video sequences. Such motion emerges from the collective behavior of individuals and their interactions and is a significant source of information typically employed for applications such as event detection, behavior recognition, and activity recognition. We present new representations of human group motion for static cameras, and propose algorithms for their application to variety of problems.We first propose a method to model and learn the scene activity of a crowd using Social Force Model for the first time in the computer vision community. We present a method to densely estimate the interaction forces between people in a crowd, observed by a static camera. Latent Dirichlet Allocation (LDA) is used to learn the model of the normal activities over extended periods of time. Randomly selected spatio-temporal volumes of interaction forces are used to learn the model of normal behavior of the scene. The model encodes the latent topics of social interaction forces in the scene for normal behaviors. We classify a short video sequence of $n$ frames as normal or abnormal by using the learnt model. Once a sequence of frames is classified as an abnormal, the regions of anomalies in the abnormal frames are localized using the magnitude of interaction forces.The representation and estimation framework proposed above, however, has a few limitations. This algorithm proposes to use a global estimation of the interaction forces within the crowd. It, therefore, is incapable of identifying different groups of objects based on motion or behavior in the scene. Although the algorithm is capable of learning the normal behavior and detects the abnormality, but it is incapable of capturing the dynamics of different behaviors.To overcome these limitations, we then propose a method based on the Lagrangian framework for fluid dynamics, by introducing a streakline representation of flow. Streaklines are traced in a fluid flow by injecting color material, such as smoke or dye, which is transported with the flow and used for visualization. In the context of computer vision, streaklines may be used in a similar way to transport information about a scene, and they are obtained by repeatedly initializing a fixed grid of particles at each frame, then moving both current and past particles using optical flow. Streaklines are the locus of points that connect particles which originated from the same initial position.This approach is advantageous over the previous representations in two aspects: first, its rich representation captures the dynamics of the crowd and changes in space and time in the scene where the optical flow representation is not enough, and second, this model is capable of discovering groups of similar behavior within a crowd scene by performing motion segmentation. We propose a method to distinguish different group behaviors such as divergent/convergent motion and lanes using this framework. Finally, we introduce flow potentials as a discriminative feature to recognize crowd behaviors in a scene. Results of extensive experiments are presented for multiple real life crowd sequences involving pedestrian and vehicular traffic.The proposed method exploits optical flow as the low level feature and performs integration and clustering to obtain coherent group motion patterns. However, we observe that in crowd video sequences, as well as a variety of other vision applications, the co-occurrence and inter-relation of motion patterns are the main characteristics of group behaviors. In other words, the group behavior of objects is a mixture of individual actions or behaviors in specific geometrical layout and temporal order.We, therefore, propose a new representation for group behaviors of humans using the inter-relation of motion patterns in a scene. The representation is based on bag of visual phrases of spatio-temporal visual words. We present a method to match the high-order spatial layout of visual words that preserve the geometry of the visual words under similarity transformations. To perform the experiments we collected a dataset of group choreography performances from the YouTube website. The dataset currently contains four categories of group dances.
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Date Issued
2011
Identifier
CFE0004482, ucf:49317
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004482