Empirical model-based control for end milling process

The main objective of this research is to develop an empirical model-based control mechanism to maintain a fine surface finish quality by maintaining on-line cutting force values. The proposed model has been developed to present the control model constraints, by varying the machining parameters to control the force output to be constant. To relate the surface finish and the cutting force in the end milling machining process, a design of experiment has been conducted to determine the effect of two different materials (aluminium and steel) and the machining parameters (feed rate, spindle speed) at a predefined depth of cut. Regression model has been applied to derive an empirical relationship of the surface finish and the cutting force versus the machining parameters for the two mentioned materials. These relationships have been applied to develop the proposed mathematical simulation model, in which the cutting force is adjusted to improve the required surface finish for the end milling operation process. The results provide means of greater efficiency by improving the surface quality, minimizing the effect of the process variablity and reducing the error cost in finishing operations

Deep learning for quantitative motion tracking based on optical coherence tomography

Optical coherence tomography (OCT) is a cross-sectional imaging modality based on low coherence light interferometry. OCT has been widely used in diagnostic ophthalmology and has found applications in other biomedical fields such as cancer detection and surgical guidance. In the Laboratory of Biophotonics Imaging and Sensing at New Jersey Institute of Technology, we developed a unique needle OCT imager based on a single fiber probe for breast cancer imaging. The needle OCT imager with sub-millimeter diameter can be inserted into tissue for minimally invasive in situ breast imaging. OCT imaging provides spatial resolution similar to histology and has the potential to become a device to perform virtual biopsy to fast and accurate breast cancer diagnosis, because abnormal breast tissue and normal breast tissue have different characteristics in OCT image. The morphological features of OCT image are related to the microscopic structure of the tissue and the speckle pattern in OCT image is related to cellular/subcellular optical properties of the tissue. In addition, depth attenuation of OCT signal depends on the scattering and absorption properties of the tissue. However, the above described OCT image features are at different spatial scales and it is challenging for human visualization to effectively recognize these features for tissue classification. Particularly, our needle OCT imager, given its simplicity and small form factor, does not have a mechanical scanner for beam steering and relies on manual scan to generate 2D images. The nonconstant translation speed of the probe in manual scanning inevitably introduces distortion artifacts in OCT imaging, which further complicates the tissue characterization task.] OCT images of tissue samples provide comprehensive information about the morphology of normal and unhealthy tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer biology. Classification of tissue images and recovering distorted OCT images are two common tasks in tissue image analysis. In this master thesis project, a novel deep learning approach is investigated to extract beam scanning speed from different samples. Furthermore, a novel technique is investigated and tested to recover distorted OCT images. The long-term goal of this study is to achieve robust tissue classification for breast cancer diagnosis, based on a simple single fiber OCT instrument. The deep learning network utilized in this study depends on Convolutional Neural Network (CNN) and Naïve Bayes Classifier. For image retrieval, we used algorithms that extract, represent and match common features between images. The CNN network achieved accuracy of 97% in tissue type and scanning speed classification, while the image retrieval algorithms achieved very high-quality recovered image compared to the reference image

Resilience Enhancement Strategies for Modern Power Systems

The frequency of extreme events (e.g., hurricanes, earthquakes, and floods) and man-made attacks (cyber and physical attacks) has increased dramatically in recent years. These events have severely impacted power systems ranging from long outage times to major equipment (e.g., substations, transmission lines, and power plants) destructions. Also, the massive integration of information and communication technology to power systems has evolved the power systems into what is known as cyber-physical power systems (CPPSs). Although advanced technologies in the cyber layer improve the operation and control of power systems, they introduce additional vulnerabilities to power system performance. This has motivated studying power system resilience evaluation and enhancements methods. Power system resilience can be defined as ``The ability of a system to prepare for, absorb, adapt to, and recover from disruptive events''. Assessing resilience enhancement strategies requires further and deeper investigation because of several reasons. First, enhancing the operational and planning resilience is a mathematically involved problem accompanied with many challenges related to modeling and computation methods. The complexities of the problem increases in CPPSs due to the large number and diverse behavior of system components. Second, a few studies have given attention to the stochastic behavior of extreme events and their accompanied impacts on the system resilience level yielding less realistic modeling and higher resilience level. Also, the correlation between both cyber and physical layers within the context of resilience enhancement require leveraging sophisticated modeling approaches which is still under investigation. Besides, the role of distributed energy resources in planning-based and operational-based resilience enhancements require further investigation. This calls for developing enhancement strategies to improve resilience of power grids against extreme events. This dissertation is divided into four parts as follows. Part I: Proactive strategies: utilizing the available system assets to prepare the power system prior to the occurrence of an extreme event to maintain an acceptable resilience level during a severe event. Various system generation and transmission constraints as well as the spatiotemporal behavior of extreme events should be properly modeled for a feasible proactive enhancement plan. In this part, two proactive strategies are proposed against weather-related extreme events and cyber-induced failure events. First, a generation redispatch strategy is formulated to reduce the amount of load curtailments in transmission systems against hurricanes and wildfires. Also, a defensive islanding strategy is studied to isolate vulnerable system components to cyber failures in distribution systems. Part II: Corrective strategies: remedial actions during an extreme event for improved performance. The negative impacts of extreme weather events can be mitigated, reduced, or even eliminated through corrective strategies. However, the high stochastic nature of resilience-based problem induces further complexities in modeling and providing feasible solutions. In this part, reinforcement learning approaches are leveraged to develop a control-based environment for improved resilience. Three corrective strategies are studied including distribution network reconfiguration, allocating and sizing of distributed energy resources, and dispatching reactive shunt compensators. Part III: Restorative strategies: retain the power service to curtailed loads in a fast and efficient means after a diverse event. In this part, a resilience enhancement strategy is formulated based on dispatching distributed generators for minimal load curtailments and improved restorative behavior. Part IV: Uncertainty quantification: Impacts of uncertainties on modeling and solution accuracy. Though there exist several sources of stochasticity in power systems, this part focuses on random behavior of extreme weather events and the associated impacts on system component failures. First, an assessment framework is studied to evaluate the impacts of ice storms on transmission systems and an evaluation method is developed to quantify the hurricane uncertainties for improved resilience. Additionally, the role of unavailable renewable energy resources on improved system resilience during extreme hurricane events is studied. The methodologies and results provided in this dissertation can be useful for system operators, utilities, and regulators towards enhancing resilience of CPPSs against weather-related and cyber-related extreme events. The work presented in this dissertation also provides potential pathways to leverage existing system assets and resources integrated with recent advanced computational technologies to achieve resilient CPPSs

Using a Learner-Centered Approach to Develop an Educational Technology Course

The article explores the structure of a graduate educational technology course that used a learner-centered approach to prepare students to be independent responsible learners. Key features of this approach were the balance of power between the instructor and students, involving students in decision-making about their learning, sharing the responsibility for learning between the instructor and students, and using students\u27 needs and interests in the course content. The article describes how the decision-making power was shared between the instructor and students, as well as how students responded to the course structure. This work has implications for creating learner-centered environments in which power and responsibility are shared between instructor and students in all graduate education courses to nurture the development of responsible learners

How to merge courses via Skype™†? Lessons from an International Blended Learning Project‡

This study reports on an international project in which students taking the course Contemporary Issues in Turkish Politics in spring 2011 and fall 2011 at two institutions of higher education, ‘Gettysburg College’ in the United States and ‘Izmir University of Economics’ in Turkey, worked together in virtual learning environments to complete various tasks as part of their course work. The project employed a blend of traditional and technology-based teaching methods in order to introduce a technology like Skype in a bi-national learning environment in Turkey. Students collaborated and interacted with their international counterparts in two different virtual contexts. First, classrooms in the two countries were merged via Skype three times to conduct classroom-to-classroom discussion sessions on Turkish politics. Second, students were paired across locations to work on several assignments. In this paper, our goal is to present how Skype is used in a bi-national context as a blended teaching tool in an upper-level college course for instructors pursuing a similar exercise. In addition to outlining the process with a focus on Skype discussions and one-on-one student projects, we provide actual assignments and discussion questions. Students’ views elicited through surveys administered throughout the semester are presented alongside anecdotal evidence to reflect how the project was received

MXenes: A New Family of Two-Dimensional Materials and its Application as Electrodes for Li-ion Batteries

Two-dimensional, 2D, materials, such as graphene, possess a unique morphology compared to their 3D counterparts, from which interesting and novel properties arise. Currently, the number of non-oxide materials that have been exfoliated is limited to two fairly small groups, viz. hexagonal, van der Waals bonded structures (e.g. graphene and BN) and layered transition metal chalcogenides. The MAX phases are a well established family of layered ternary transition metal carbides and/or nitrides, with a composition of Mn+1AXn, where M is an early transition metal, A is one of A group elements, X is C and/or N; with n = 1, 2, or 3. The aim of this work is to exfoliate the MAX phases and produce 2D layers of transition metals carbides and/or nitrides by the selective etching of the A layers from the MAX phases. We labeled the resulting 2D Mn+1Xn layers "MXenes" to emphasize the loss of the A group element from the MAX phases and the suffix "ene" to emphasize their 2D nature and their similarity to graphene. The etching process was carried out using aqueous hydrofluoric acid at room temperature. Thirteen different MXenes were produced as a result of this work, viz., Ti2C, Nb2C, V2C, Mo2C, (Ti0.5,Nb0.5)2C, (Ti0.5,V0.5)2C, Ti3C2, (Ti0.5,V0.5)3C2, (V0.5,Cr0.5)3C2, Ti3CN, Ta4C3, Nb4C3 and (Nb0.5,V0.5)4C3. The as-synthesized MXenes were terminated with a mixture of OH, O, and/or F groups. Sonicating MXenes resulted in separating the stacked layers to a small extent. When Ti3C2 was intercalated with dimethylsulfoxide, however, followed by sonication in water, large-scale delamination occurred, which resulted in aqueous colloidal solutions that could in turn be fabricated into MXene "paper". MXenes were found to be electrically conductive, hydrophilic and stable in aqueous environments, a rare combination indeed, with huge potential in many applications, from energy storage, to sensors to catalysts. This work focused on the use of MXenes as electrode materials in Li-ion batteries. They exhibited excellent capability to handle high cycling rates with good gravimetric capacities. The lithiation and delithiation were found to be due to redox intercalation/deintercalation reactions.Ph.D., Materials Science and Engineering -- Drexel University, 201

Cyber-Physical Power System Layers: Classification, Characterization, and Interactions

This paper provides a strategy to identify layers and sub-layers of cyber-physical power systems (CPPS) and characterize their inter- and intra-actions. The physical layer usually consists of the power grid and protection devices whereas the cyber layer consists of communication, and computation and control components. Combining components of the cyber layer in one layer complicates the process of modeling intra-actions because each component has different failure modes. On the other hand, dividing the cyber layers into a large number of sub-layers may unnecessarily increase the number of system states and increase the computational burden. In this paper, we classify system layers based on their common, coupled, and shared functions. Also, interactions between the classified layers are identified, characterized, and clustered based on their impact on the system. Furthermore, based on the overall function of each layer and types of its components, intra-actions within layers are characterized. The strategies developed in this paper for comprehensive classification of system layers and characterization of their inter- and intra-actions contribute toward the goal of accurate and detailed modeling of state transition and failure and attack propagation in CPPS, which can be used for various reliability assessment studies.Comment: Accepted in Texas Power and Energy Conference (TPEC) 202

Soil structure interaction for shrink-swell soils a new design procedure for foundation slabs on shrink-swell soils

Problems associated with shrink-swell soils are well known geotechnical problems that have been studied and researched by many geotechnical researchers for many decades. Potentially shrink-swell soils can be found almost anywhere in the world especially in the semi-arid regions of the tropical and temperate climate. Foundation slabs on grade on shrink-swell soils are one of the most efficient and inexpensive solutions for this kind of problematic soil. It is commonly used in residential foundations or any light weight structure on shrink-swell soils. Many design methods have been established for this specific problem such as Building Research Advisory Board (BRAB), Wire Reinforcement Institute (WRI), Post- Tensioning Institute (PTI), and Australian Standards (AS 2870) design methods. This research investigates most of these methods, and then, proposes a moisture diffusion soil volume change model, a soil-weather interaction model, and a soil-structure interaction model. The proposed moisture diffusion soil volume change model starts with proposing a new laboratory test to determine the coefficient of unsaturated diffusivity for intact soils. Then, it introduces the development of a cracked soil diffusion factor, provides a chart for it, and explains a large scale laboratory test that verifies the proposed moisture diffusion soil volume change model. The proposed soil-weather interaction model uses the FAO 56-PM method to simulate a weightless cover performance for six cities in the US that suffer significantly from shallow foundation problems on shrink-swell soils due to seasonal weather variations. These simulations provide more accurate weather site-specific parameters such as the range of surface suction variations. The proposed weather-site specific parameters will be input parameters to the soil structure models. The proposed soil-structure interaction model uses Mitchell (1979) equations for moisture diffusion under covered soil to develop a new closed form solution for the soil mound shape under the foundation slab. Then, it presents a parametric study by carrying out several 2D finite elements plane strain simulations for plates resting on a semiinfinite elastic continuum and resting on different soil mounds. The parametric study outcomes are then presented in design charts that end with a new design procedure for foundation slabs on shrink-swell soils. Finally, based on the developed weather-soil-structure interaction models, this research details two procedures of a proposed new design method for foundation slabs on grade on shrink-swell soils: a suction based design procedure and a water content based design procedure