| 22741. |
- Kim, Youngmin
(author)
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In Memoriam : Critic par excellence of Creative "Unoriginal Genius" & Marjorie Perloff's Arcades Project of Poetry by "Other Means" (1951-2024)
- 2024
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In: Interdisciplinary Studies of Literature. - 2520-4920 .- 2616-4566. ; 8:2, s. 331-337
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Journal article (peer-reviewed)abstract
- Wittgenstein reflects on memory, saying that photograph is not reliable, and the memory-image cannot convince us either, since "memory does not show us the past, any more than our senses show us the present," and "memory is itself conditioned by the specificity of context." Reading closely the conceptual artist Joseph Kosuth's large art exhibition, called "The Play of the Unsayable," Marjorie Perloff relates Wittgenstein's theory of language game to Kosuth's art text of Abridged in Ghent, and argues that the language game initiated by a sentence like "I see us still, sitting at the table" is charged with possibilities for "philosophy" as a "form of poetic composition." Wittgenstein's Ladder is an apt figure for Marjorie Perloff's radical aesthetic which is ethical as well, doing the right thing for the individual poets, moving up the ladder which Gertrude Stein called "beginning again and again," but with changes with repetition in a spiral way. Later in her preface of Unoriginal Genius: Poetry by Other Means in the New Century (2010), Marjorie provides her rationale to update her earlier work, Radical Artifice: Writing Poetry in the Age of Media (1991) in terms of a "new citational and often constrained-bound poetry" in an environment of "hyper-information." Since Unoriginal Genius (2010), Perloff traces her poetics of "unoriginal genius" from a Benjamian Arcades Project, made up of creative citations, discussing the processes of choice, framing, and reconfiguration. It is my contention that Marjorie Perloff as the critic par excellence has been dedicating her own Arcades Project to explore the intriguing development in contemporary poetry, creatively embracing the "unoriginal" writing of uncreative poets (Email: yk4147@ gmail.com).
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| 22742. |
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| 22743. |
- Kindvall, O., et al.
(author)
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Subsidized Common Agricultural Policy grazing jeopardizes the protection of biodiversity and Natura 2000 targeted species
- 2022
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In: Animal Conservation. - : John Wiley & Sons. - 1367-9430 .- 1469-1795. ; 25:5, s. 597-607
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Journal article (peer-reviewed)abstract
- In Europe, Natura 2000 sites should protect threatened target species and networks of habitats. The management of Natura 2000 grasslands is often financed by subsidized grazing as part of the Common Agricultural Policy (CAP). We studied the extent of CAP grazing for Natura 2000 management and how this affects a butterfly target species (the marsh fritillary) and floral resources. Based on extensive capture-mark-release studies from 2 years in >550 ha grid cells in a 225 km(2) landscape in Sweden that includes 15 Natura 2000 sites, we compared marsh fritillary occurrence probabilities and population densities in ungrazed and CAP-grazed habitats. Moreover, we analyzed how nectar resources and orchids were affected by CAP grazing based on plants records from 2347 sample plots. We estimated the proportion of butterfly habitats that were CAP-grazed within and outside Natura 2000 sites. In total, 10 453 and 4417 butterflies were marked in 2017 and 2019, respectively. The grid cell occurrence probability was 1.8 times higher and the population density was 2.3 times higher in ungrazed compared with CAP-grazed habitats in 2017, and the corresponding numbers for 2019 were 10 and 5.3 times higher, respectively. The number of flowering plants were on average 6.9 times higher and the density of orchids was 12.3 times higher in ungrazed habitats. Roughly, 30% (130 ha) of the marsh fritillary habitat was CAP grazed, and 97% of this grazing occurred within protected areas, of which 111 ha was situated within Natura 2000 area where the marsh fritillary is the target species. Alarmingly, we show that intense yearly CAP grazing, which is the dominant management strategy in all Natura 2000 sites, has devastating consequences for the target species and other aspects of biodiversity. Less intense management, which would benefit biodiversity, requires changes in the CAP, to allow more flexible payments for habitat management objectives and conservation of target species.
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| 22744. |
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| 22745. |
- Kinsey, Dianne Caracuzzo, et al.
(author)
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Nursing education and mental health care in Sweden : impressions from a faculty exchange with the University of Växjö
- 2001
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In: Nursing and Health Care Perspectives. - : National League for Nursing. - 1094-2831. ; 22:3, s. 136-139
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Journal article (pop. science, debate, etc.)abstract
- A SABBATICAL LEAVE during the spring of 1999 has led to an ongoing exchange program between CEDAR CREST COLLEGE, a small women's liberal arts college located in northeastern Pennsylvania, and the UNIVERSITY OF VÄXJÖ, a major regional university in southeastern Sweden. The author visited the University of Växjö to pursue her research interest in transcultural nursing and health care and to exchange information on nursing education and practice, with a focus on mental health nursing and health care. Her long-range goal was to develop further faculty and student exchanges.The first author instituted an ongoing exchange program for nursing students and faculty in a small women's liberal arts college in eastern Pennsylvania and a regional university in southeastern Sweden. A site visit to Sweden in 1999 initiated discussion of differences in educational methodologies and mental health care between Sweden and the United States. This visit served as the basis for continued interaction between the two institutions, including a faculty and student visit in 2001 that has broadened the scope of study beyond mental health care.
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| 22746. |
- Kiranyaz, Serkan, et al.
(author)
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1-D Convolutional Neural Networks for Signal Processing Applications
- 2019
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In: 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing. - : IEEE. - 9781479981311 - 9781479981328 ; , s. 8360-8364
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Conference paper (peer-reviewed)abstract
- 1D Convolutional Neural Networks (CNNs) have recently become the state-of-the-art technique for crucial signal processing applications such as patient-specific ECG classification, structural health monitoring, anomaly detection in power electronics circuitry and motor-fault detection. This is an expected outcome as there are numerous advantages of using an adaptive and compact 1D CNN instead of a conventional (2D) deep counterparts. First of all, compact 1D CNNs can be efficiently trained with a limited dataset of 1D signals while the 2D deep CNNs, besides requiring 1D to 2D data transformation, usually need datasets with massive size, e.g., in the "Big Data" scale in order to prevent the well-known "overfitting" problem. 1D CNNs can directly be applied to the raw signal (e.g., current, voltage, vibration, etc.) without requiring any pre- or post-processing such as feature extraction, selection, dimension reduction …
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| 22747. |
- Kiranyaz, Serkan, et al.
(author)
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1D convolutional neural networks and applications : A survey
- 2021
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In: Mechanical systems and signal processing. - : Elsevier. - 0888-3270 .- 1096-1216. ; 151
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Journal article (peer-reviewed)abstract
- During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and electrical motor fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publicly shared in a dedicated website. While there has not been a paper on the review of 1D CNNs and its applications in the literature, this paper fulfills this gap. (C) 2020 The Author(s). Published by Elsevier Ltd.
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| 22748. |
- Kiranyaz, Serkan, et al.
(author)
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Zero-shot motor health monitoring by blind domain transition
- 2024
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In: Mechanical systems and signal processing. - : Elsevier. - 0888-3270 .- 1096-1216. ; 210
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Journal article (peer-reviewed)abstract
- Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the recent deep learning (DL) methodologies trained on the labeled data from the same machine, the classification accuracy significantly deteriorates when one or few conditions are altered, e.g., a different speed or load, or for different fault types/severities with sensors placed in different locations. Furthermore, their performance suffers significantly or may entirely fail when they are tested on another machine with entirely different healthy and faulty signal patterns. To address this need, in this pilot study, we propose a zero -shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics. To accomplish this objective, a 1D Operational Generative Adversarial Network (Op-GAN) first characterizes the transition between normal and fault vibration signals of (a) source machine(s) under various conditions, sensor parameters, and fault types. Then for a target machine, the potential faulty signals can be generated, and over its actual healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN fault detector can then be trained to detect the real faulty condition in real time whenever it occurs. To validate the proposed approach, a new benchmark dataset is created using two different motors working under different conditions and sensor locations. Experimental results demonstrate that this novel approach can accurately detect any bearing fault achieving an average recall rate of around 89% and 95% on two target machines regardless of its type, severity, and location.
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| 22749. |
- Kirchner, Jens, et al.
(author)
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Appropriate machine learning methods for service recommendation based on measured consumer experiences within a service market
- 2015
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In: SERVICE COMPUTATION 2015 : The Seventh International Conferences on Advanced Service Computing, March 22, 2015 to March 27, 2015, Nice, France. - : International Academy, Research and Industry Association (IARIA). - 9781612083872 ; , s. 41-48
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Conference paper (peer-reviewed)abstract
- The actual experience of the performance of services at consumers’ side is a desirable foundation for service selection. Considering the knowledge of previous performance experiences from a consumer’s perspective, a service broker can automatically select the best-fitting service out of a set of functionally similar services. In this paper, we present the evaluation of machine learning methods and frameworks which can be employed for service recommendation based on shared experiences of previous consumers. Implemented in a prototype, our approach considers a consumer’s call context as well as its selection preferences (expressed in utility functions). The implementation of the framework aims at the time-critical optimisation of service consumption with focus on runtime aspects and scalability. Therefore, we evaluated and employed high-performance, online and large scale machine learning methods and frameworks. Considering the Internet as a service market with perpetual change, strategies for concept drift have to be found. The evaluation showed that with the current approach, the framework recommended the actual best-fit service instance in 70% of the validation cases, while in 90% of the cases, the best or second best-fit was recommended. Furthermore, within our approach employing the best method, we achieved 94.5% of the overall maximum achievable utility value.
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| 22750. |
- Kirchner, Jens, et al.
(author)
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Classification vs. Regression : Machine learning approaches for service recommendation based on measured consumer experiences
- 2015
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In: IEEE World Congress on Services (SERVICES), 2015. - : IEEE Press. - 9781467372749 ; , s. 278-285
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Conference paper (peer-reviewed)abstract
- Service functionality can be provided by more than one service consumer. In order to choose the service which creates the most benefit before its consumption, a selection based on previous measurable experiences by other consumers is beneficial. In this paper, we present the results of our analysis of two machine learning approaches to predict the best service within this selection problem. The first approach focuses on classification, predicting the best performing service, while the second approach focuses on regression, predicting service performances which can then be used for the determination of the best candidate. We assessed and compared both approaches for service recommendation w.r.t. The performance gain when selecting the recommended instead of a random service. Our evaluation is based on data measured on real Web services as well as on simulated data. The latter is needed for a more profound analysis of the strengths and weaknesses of each approach. The simulated data has similar statistical properties as the data measured on real Web services. In the real-world case, regression achieved a response time gain of over 92% of the optimum and classification over 83%. In case of simulated data, we could achieve an overall gain of up to 95% using classification, while regression achieved 89%.
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