A registration-free approach for image monitoring


 
 
 The development of robust monitoring systems for assuring the consistency and stability of multistage manufacturing processes necessitates the use of add-on sensors and advanced data collection, storage, and analysis platforms to deal with the high-dimensional data collected from machines and products in multiple stages. In many cases, such an approach may not be feasible due to high implementation costs and the challenges of obtaining the process parameters and analyzing them effectively. This paper proposes an alternative approach for health monitoring and diagnosis of multistage manufacturing processes based on product quality measurements in a sensor-less environment. In the presented work, the available data consists of product quality parameters measured from multiple product types along with the manufacturing route associated with each product. A Gamma distribution is fit to the data for each parameter within a moving time window. Using the distribution fits, a metric is developed to represent the performance of each machine in a stage compared to its peers producing the same product. This metric is then aggregated across all the products produced by the machine to generate the final metric reflecting the overall performance of the machine. This performance metric is first calculated for the machines in the last stage. After flagging the underperforming machines in the last stage, the samples from those machines are removed from the data set and the remaining samples are used to calculate the similar metric for the prior stage. The suggested approach assumes the random distribution of products from one stage to the next to facilitate the implementation of a comparison-based approach. This approach is tested on a data set collected from a manufacturing plant. The results demonstrate the effectiveness of such approach for monitoring and diagnosis of multistage manufacturing processes when the data is not available from within the process.
 
 

A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant

Chapter 25 - Cardiovascular Disease

In December 2010, parties to the United Nations Framework Convention on Climate Change (UNFCCC) agreed to encourage reductions in greenhouse gas emissions from forest losses with the financial support of developed countries. This important international agreement followed about seven years of effort among governments, non-governmental organizations (NGO) and the scientific community, and is called REDD+, the program for Reducing Emissions from Deforestation and Forest Degradation. REDD+ could achieve its potential to slow emissions from deforestation and forest degradation either as a new market option to offset emissions from developed nations, or as a mitigation option for developing countries themselves. Aside from representing an important step towards reducing greenhouse gas emissions, a growing list of potential co-benefits to REDD+ include improved forestry practices, forest restoration, sustainable development, and biodiversity protection. Indeed the agreement is heralded as a win–win for climate change mitigation and tropical forest conservation, and it could end up contributing to a global economy based on carbon and ecosystem services.

During the 1970s the U.S. Environmental Protection Agency (EPA) devised a multiscale system of basin planning and regional implementation that encouraged a balanced mixture of monitoring and modeling-based assessments. By the 1980s, this goal had not been achieved. Modeling and monitoring assessment approaches became largely decoupled. To a significant degree, modeling was viewed as too inaccurate to handle issues such as setting permit limits involving toxics. During the 1980s, EPA also encouraged the idea that monitoring approaches were adequate to document water quality problems, guide the development of management plans, and demonstrate the achievement of management goals. By the late 1990s, large numbers of waters listed under the Clean Water Act's Total Maximum Daily Load (TMDL) provisions showed the widespread nature of pollutant concerns, but the uneven nature of the listing information also revealed fundamental problems in the ability of state monitoring programs to achieve credible and comprehensive assessments. Statistics are presented from the 1998 and the most current publicly available 2000 baseline periods showing the limitations in the scope of state assessments. There are significant opportunities for the increased use of relatively simple modeling systems that can be flexibly implemented over a variety of spatial scales. In addition to conventional modeling frameworks, the value of bioassessment monitoring techniques is stressed. Bioassessment indicators can often be combined with landscape modeling methods, as well as analyses from conventional modeling outputs, to help target small area monitoring by use of tiered approaches. These findings underscore the value of integrated monitoring and modeling approaches to build properly balanced assessment systems.

Buffer overflow (BOF) is a well-known, and one of the worst and oldest, vulnerabilities in programs. BOF attacks overwrite data buffers and introduce wide ranges of attacks like execution of arbitrary injected code. Many approaches are applied to mitigate buffer overflow vulnerabilities; however, mitigating BOF vulnerabilities is a perennial task as these vulnerabilities elude the mitigation efforts and appear in the operational programs at run-time. Monitoring is a popular approach for detecting BOF attacks during program execution, and it can prevent or send warnings to take actions for avoiding the consequences of the exploitations. Currently, there is no detailed classification of the proposed monitoring approaches to understand their common characteristics, objectives, and limitations. In this paper, the authors classify runtime BOF attack monitoring and prevention approaches based on seven major characteristics. Finally, these approaches are compared for attack detection coverage based on a set of BOF attack types. The classification will enable researchers and practitioners to select an appropriate BOF monitoring approach or provide guidelines to build a new one.

Current State of Drug Therapies for Antibody-mediated Rejection After Heart Transplantation.

"Condition monitoring of machinery in industries is becoming an emerging technique for efficient operations and productivity. A wide range of fault diagnosis approaches have been proposed to improve machinery operations in industries. These monitoring and fault diagnosis approaches are most effective for reliable machinery operations. However, due to the harsh environment of industries, these approaches have been suffered from different challenges. In order to extract the significant features for fault diagnosis and monitoring, different neural, fuzzy, and signal processing based systems were adopted. In this paper, we discuss wireless sensor based fault diagnosis and monitoring approaches and their types for industrial machinery. Furthermore, the paper presents industrial challenges to adopting these approaches and related efforts."

Direct laser interference patterning (DLIP) is a promising technique for fabricating periodic surface structures on large areas, but controlling process quality can be challenging due to laser power fluctuations. This study presents a novel monitoring approach using acoustic and optical emissions to diagnose and correct these fluctuations during DLIP structure fabrication. Experiments are conducted using a nanosecond‐pulsed IR laser to create 6 μm periodic microstructures on stainless steel. Significant laser power fluctuations are observed over time, resulting in noticeable surface texture inhomogeneity across larger areas. Acoustic and optical emissions, recorded via microphone and photodiodes respectively, are found to correlate well with local structure depth, surface roughness, and macroscopic appearance of the textured surface. A strategy for in‐process correction is demonstrated using a PI controller to adjust laser power based on acoustic emission feedback in the 18–22 kHz range during processing. Implementing this closed‐loop control system achieves a homogeneous texture with a consistent 2 μm structure depth, compared to significant variations between 0.8 and 2.4 μm without the controller. This monitoring and control approach offers a simple, cost‐effective solution for ensuring quality and consistency in large‐area DLIP fabrication processes, potentially improving the reliability and efficiency of surface texturing applications.

This chapter describes the monitoring approaches used by marine environmental management frameworks for the assessment of plastic pollution in the European sea basins: Mediterranean Sea, Black Sea, Baltic Sea and the North-East Atlantic region including the North Sea. We review the current developments in the implementation of monitoring elements under the European Marine Strategy Framework Directive and the four related Regional Sea Conventions: OSPAR Commission, HELCOM, Black Sea Commission and UNEP/MAP Barcelona Convention. The analysis of the existing monitoring approaches reveals strong commitments to providing common monitoring elements for the assessment of marine litter across the different management frameworks and, consequently, between neighbouring countries. Although these monitoring approaches rely mostly on a series of common monitoring elements, the implementation of such approaches is recent and remains subject to relevant knowledge and data gaps. Nevertheless, interaction and coordination among management frameworks will provide substantial improvements to achieving coherent and comparable assessments for marine litter and plastic pollution across Europe in the coming years. The process of developing and implementing monitoring approaches at the European scale through international cooperation is an excellent source of knowledge and experience for the scientific community and policy-makers on a global scale.

Predictive on-line monitoring of continuous processes

Internet of Things and citizen science as alternative water quality monitoring approaches and the importance of effective water quality communication

Assessing environmental quality often requires selection of indicators that can be employed over large spatial scales and over long-time periods to assess the health and well-being of species, natural communities, and ecosystems, and to detect changes warranting intervention. Typically, the ecologic environment and the human environment are evaluated separately and selection of indicators and monitoring approaches are not integrated even though ecological indicators may also provide information on risk to human consumers from contaminants (e.g., eco-cultural indicators) or because of disease levels. This paper is a call for ecologists and managers to consider diverse cultural and environmental injustice disparities and health issues when selecting indicators for environmental assessment and monitoring. There is an opportunity for managers and community members to work together to preserve ecological and cultural resources and heritages. We propose a paradigm that selects indicators and monitoring approaches that lend themselves to the integration of human-diversity and uniqueness in the same manner that the selection of ecological indicators and monitoring approaches consider biological species diversity and uniqueness. The proposed paradigm builds on ecological risk assessment techniques, developing analogous endpoints for neighboring communities. For example, identification and protection of human communities, particularly culturally diverse and environmental justice communities, identification of contaminant corridors (e.g., through water or green corridors) into communities, and eco-monitoring of vulnerable communities are not routine at contaminated sites. Green corridors refers to a width of wild habitat (forest, grasslands) that connects other similar habitat paths (usually a corridor runs through an urban or suburban habitat). We coin the term Eco-EJ indicators for these endpoints, including examination of (1) unique cultural relationships to resources; (2) connectedness of on-site and off-site resources and habitats; (3) health of threatened, rare, and unique cultures and communities; and (4) linkages between ecological, eco-cultural, and public health for monitoring and assessment. We also propose that assessment and monitoring include these Eco-EJ indicators, especially for communities near facilities that have extensive chemical or radiologicalcontamination.Developing these indicators to assess risk to culturally diverse and environmental justice communities would be an equivalent goal to reducing risk for significant ecological resources (e.g., endangered species, species of special concern). These Eco-EJ indicators are complementary to the usual human health-risk assessments, would include surveys of neighboring vulnerable communities, and require time and re-organization of current data and additional data collection at site boundaries and in adjacent communities, as well as rethinking the human component of indicators. This approach lends itself to addressing some diverse cultural and environmental justice issues with current indicator selection and biomonitoring, and helps identify specific hotspots of unique ecosystem risk and environmental justice community risk. We briefly discuss ecological and eco-cultural monitoring already on-going at three Department of Energy sites to illustrate how the addition of these indicators might work and add value to environmental management and to their relationships with surrounding communities. We recommend that managers of contaminated sites convene people from culturally diverse communities, environmental justice communities, local and federal government, Tribes, resource trustees, managers, and other stakeholders to develop appropriate site-specific indicators to address environmental inequities around contaminated facilities.

China is facing severe climate pollution, thus the CO2 emissions of thermal power plants which consume a lot of fossil energy, need to be strictly monitored. At the same time, the thermal power plants and the government will face brand new environment, where the exactly appropriate monitoring approach of CO2 emission remains ambiguous. This study aims to distinguish monitoring approaches between Continuous Emission Monitoring System (CEMS) and factor-based approach on the basis of the operation features of China’s thermal power plants, analyzing the extension of CEMS. We review the major reducing greenhouse gas initiative in China—carbon market, and different emission monitoring approaches at first. We present the prospects of extension in CEMS’s technical features by analyzing an example of two generations using coal and gas, respectively, finding that CEMS is more accurate and dynamic. This study also presents the challenges by analyzing the refinement of factor-based monitoring approach. However, In contrast to many previous studies, we consider different influence in prospect and challenge from the market itself, the application experience and equipment installation basis. We finally draw an important conclusion that the factor-based monitoring approach is more suitable for China’s thermal power plants currently, but CEMS is more promising.

A domain analysis of resource and requirements monitoring: Towards a comprehensive model of the software monitoring domain