Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154105/1/kampfner1997.pd

Artificial neural network and its applications in quality process control, document recognition and biomedical imaging

In computer-vision based system a digital image obtained by a digital camera would usually have 24-bit color image. The analysis of an image with that many levels might require complicated image processing techniques and higher computational costs. But in real-time application, where a part has to be inspected within a few milliseconds, either we have to reduce the image to a more manageable number of gray levels, usually two levels (binary image), and at the same time retain all necessary features of the original image or develop a complicated technique. A binary image can be obtained by thresholding the original image into two levels. Therefore, thresholding of a given image into binary image is a necessary step for most image analysis and recognition techniques. In this thesis, we have studied the effectiveness of using artificial neural network (ANN) in pharmaceutical, document recognition and biomedical imaging applications for image thresholding and classification purposes. Finally, we have developed edge-based, ANN-based and region-growing based image thresholding techniques to extract low contrast objects of interest and classify them into respective classes in those applications. Real-time quality inspection of gelatin capsules in pharmaceutical applications is an important issue from the point of view of industry\u27s productivity and competitiveness. Computer vision-based automatic quality inspection and controller system is one of the solutions to this problem. Machine vision systems provide quality control and real-time feedback for industrial processes, overcoming physical limitations and subjective judgment of humans. In this thesis, we have developed an image processing system using edge-based image thresholding techniques for quality inspection that satisfy the industrial requirements in pharmaceutical applications to pass the accepted and rejected capsules. In document recognition application, success of OCR mostly depends on the quality of the thresholded image. Non-uniform illumination, low contrast and complex background make it challenging in this application. In this thesis, optimal parameters for ANN-based local thresholding approach for gray scale composite document image with non-uniform background is proposed. An exhaustive search was conducted to select the optimal features and found that pixel value, mean and entropy are the most significant features at window size 3x3 in this application. For other applications, it might be different, but the procedure to find the optimal parameters is same. The average recognition rate 99.25% shows that the proposed 3 features at window size 3x3 are optimal in terms of recognition rate and PSNR compare to the ANN-based thresholding technique with different parameters presented in the literature. In biomedical imaging application, breast cancer continues to be a public health problem. In this thesis we presented a computer aided diagnosis (CAD) system for mass detection and classification in digitized mammograms, which performs mass detection on regions of interest (ROI) followed by the benign-malignant classification on detected masses. Three layers ANN with seven features is proposed for classifying the marked regions into benign and malignant and 90.91% sensitivity and 83.87% specificity is achieved that is very much promising compare to the radiologist\u27s sensitivity 75%

Methodological framework and design process for applying evolutionary simulation to musical interactions

This paper focuses on a methodological framework where the creative design process evolves through iterative cycles. The design process undertakes a complex network of tasks for integrating two domain models: dynamical simulation and musical interaction. The framework accounts for engi-neering technical and compositional affordances to accom-modate evolving behaviors to be expressed in real time per-formance interplay. This is illustrated with a case study of simulated swarms of heterogeneous agents. Highly integrat-ed parallel work streams are elucidated with sub-process elicitation in simulation, system integration and software engineering, composition, and performance. Framework formalization draws upon the established RAD model with significant modification to present the extended version that can be multi-threaded for concurrent creative processes. Two landmarks of 20th century music automation are drawn diachronically to frame the technical discussion in a social context of listening practice, developed by modeling crea-tive process and testing musical assumptions. Revisited cannon is redirected from bygone exemplars to ongoing practice, illuminating three baseline requirements for a methodological framework: interdisciplinary platform archi-tecture, complex systems model of music creation, and agile listening. Concluding theses on second order listening and interdisciplinary architecture summarize the proposed methodological framework addressing contextual listening and technical culture

Polyelectrolyte Multilayered Capsules as Biomedical Tools

Polyelectrolyte multilayered capsules (PEMUCs) obtained using the Layer-by-Layer (LbL) method have become powerful tools for different biomedical applications, which include drug delivery, theranosis or biosensing. However, the exploitation of PEMUCs in the biomedical field requires a deep understanding of the most fundamental bases underlying their assembly processes, and the control of their properties to fabricate novel materials with optimized ability for specific targeting and therapeutic capacity. This review presents an updated perspective on the multiple avenues opened for the application of PEMUCs to the biomedical field, aiming to highlight some of the most important advantages offered by the LbL method for the fabrication of platforms for their use in the detection and treatment of different diseases

Infant Cry Signal Processing, Analysis, and Classification with Artificial Neural Networks

As a special type of speech and environmental sound, infant cry has been a growing research area covering infant cry reason classification, pathological infant cry identification, and infant cry detection in the past two decades. In this dissertation, we build a new dataset, explore new feature extraction methods, and propose novel classification approaches, to improve the infant cry classification accuracy and identify diseases by learning infant cry signals. We propose a method through generating weighted prosodic features combined with acoustic features for a deep learning model to improve the performance of asphyxiated infant cry identification. The combined feature matrix captures the diversity of variations within infant cries and the result outperforms all other related studies on asphyxiated baby crying classification. We propose a non-invasive fast method of using infant cry signals with convolutional neural network (CNN) based age classification to diagnose the abnormality of infant vocal tract development as early as 4-month age. Experiments discover the pattern and tendency of the vocal tract changes and predict the abnormality of infant vocal tract by classifying the cry signals into younger age category. We propose an approach of generating hybrid feature set and using prior knowledge in a multi-stage CNNs model for robust infant sound classification. The dominant and auxiliary features within the set are beneficial to enlarge the coverage as well as keeping a good resolution for modeling the diversity of variations within infant sound and the experimental results give encouraging improvements on two relative databases. We propose an approach of graph convolutional network (GCN) with transfer learning for robust infant cry reason classification. Non-fully connected graphs based on the similarities among the relevant nodes are built to consider the short-term and long-term effects of infant cry signals related to inner-class and inter-class messages. With as limited as 20% of labeled training data, our model outperforms that of the CNN model with 80% labeled training data in both supervised and semi-supervised settings. Lastly, we apply mel-spectrogram decomposition to infant cry classification and propose a fusion method to further improve the infant cry classification performance

Innovative solar energy technologies and control algorithms for enhancing demand-side management in buildings

The present thesis investigates innovative energy technologies and control algorithms for enhancing demand-side management in buildings. The work focuses on an innovative low-temperature solar thermal system for supplying space heating demand of buildings. This technology is used as a case study to explore possible solutions to fulfil the mismatch between energy production and its exploitation in building. This shortcoming represents the primary issue of renewable energy sources. Technologies enhancing the energy storage capacity and active demand-side management or demand-response strategies must be implemented in buildings. For these purposes, it is possible to employ hardware or software solutions. The hardware solutions for thermal demand response of buildings are those technologies that allow the energy loads to be permanently shifted or mitigated. The software solutions for demand response are those that integrate an intelligent supervisory layer in the building automation (or management) systems. The present thesis approaches the problem from both the hardware technologies side and the software solutions side. This approach enables the mutual relationships and interactions between the strategies to be appropriately measured. The thesis can be roughly divided in two parts. The first part of the thesis focuses on an innovative solar thermal system exploiting a novel heat transfer fluid and storage media based on micro-encapsulated Phase Change Material slurry. This material leads the system to enhance latent heat exchange processes and increasing the overall performance. The features of Phase Change Material slurry are investigated experimentally and theoretically. A full-scale prototype of this innovative solar system enhancing latent heat exchange is conceived, designed and realised. An experimental campaign on the prototype is used to calibrate and validate a numerical model of the solar thermal system. This model is developed in this thesis to define the thermo-energetic behaviour of the technology. It consists of two mathematical sub-models able to describe the power/energy balances of the flat-plate solar thermal collector and the thermal energy storage unit respectively. In closed-loop configuration, all the Key Performance Indicators used to assess the reliability of the model indicate an excellent comparison between the system monitored outputs and simulation results. Simulation are performed both varying parametrically the boundary condition and investigating the long-term system performance in different climatic locations. Compared to a traditional water-based system used as a reference baseline, the simulation results show that the innovative system could improve the production of useful heat up to 7 % throughout the year and 19 % during the heating season. Once the hardware technology has been defined, the implementation of an innovative control method is necessary to enhance the operational efficiency of the system. This is the primary focus of the second part of the thesis. A specific solution is considered particularly promising for this purpose: the adoption of Model Predictive Control (MPC) formulations for improving the system thermal and energy management. Firstly, this thesis provides a robust and complete framework of the steps required to define an MPC problem for building processes regulation correctly. This goal is reached employing an extended review of the scientific literature and practical application concerning MPC application for building management. Secondly, an MPC algorithm is formulated to regulate the full-scale solar thermal prototype. A testbed virtual environment is developed to perform closed-loop simulations. The existing rule-based control logic is employed as the reference baseline. Compared to the baseline, the MPC algorithm produces energy savings up to 19.2 % with lower unmet energy demand

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference