• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.5
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• pp.638-644
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• 2022

The measurement of morphological features is essential in aquaculture, fish industry and the management of fishery resources. The measurement of fish requires a large investment of manpower and time. To save time and labor for fish measurement, automated and reliable measurement methods have been developed. Automation was achieved by applying computer vision and machine learning techniques. Recently, machine learning methods based on deep learning have been used for most automatic fish measurement studies. Here, we review the current status of automatic fish measurement with traditional computer vision methods and deep learning-based methods.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.253-260
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• 2022

Existing web-based cost databases have proved invaluable for construction cost estimating. These databases have been utilized to compute approximate cost estimates using assembly rates, unit rates, and etc. These web-based databases can be used independently with traditional cost estimation methods (manual methods) or used to support BIM-based cost estimating platforms. However, these databases are rigid, costly, and require a lot of manual inputs to reflect recent trends in prices or prices relative to a construction project's location. To address this gap, this study integrated deep learning techniques with web-based price analysis to develop a database that incorporates a project's location cost estimating standards and current cost trends in generating a cost estimate. The proposed method was tested in a case study project in Lagos, Nigeria. A cost estimate was successfully generated. Comparison of the experimental results with results using current industry standards showed that the proposed method achieved a 98.16% accuracy. The results showed that the proposed method was successful in generating approximate cost estimates irrespective of project's location.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1507-1508
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• 2013

• Nuclear Engineering and Technology
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• v.52 no.9
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• pp.1998-2008
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• 2020

Solenoid operated valves (SOV) play important roles in industrial process to control the flow of fluids. Solenoid valves can be found in so many industries as well as the nuclear plant. The ability to be able to detect the presence of faults and predicting the remaining useful life (RUL) of the SOV is important in maintenance planning and also prevent unexpected interruptions in the flow of process fluids. This paper proposes a fault diagnosis method for the alternating current (AC) powered SOV. Previous research work have been focused on direct current (DC) powered SOV where the current waveform or vibrations are monitored. There are many features hidden in the AC waveform that require further signal analysis. The analysis of the AC powered SOV waveform was done in the time and frequency domain. A total of sixteen features were obtained and these were used to classify the different operating modes of the SOV by applying a machine learning technique for classification. Also, a deep neural network (DNN) was developed for the prediction of RUL based on the failure modes of the SOV. The results of this paper can be used to improve on the condition based monitoring of the SOV.

• Ocean and Polar Research
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• v.28 no.4
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• pp.451-457
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• 2006

Hole 1124 of ODP Leg 181 was located in the Rekohu sediment drift off eastern New Zealand in the southwest Pacific Ocean. Mean gain sizes of sortable silt were measured in two drilled cores (1124A and l124B). Chronostratigraphy of core 1124 was correlated with the well-dated nearby core S931, resulting that the age of core 1124 covers the late Pleistocene spanning about MIS (Marine Isotope Stage) 5. Mean grain size of sortable silt seemed to be relatively large during the glacial period, whereas that of the interglacial period was smaller, although several tephra layers contain some coarse-grained pyroclatic particles. The variation in mean grain size of sortable silt in Rekohu sediment drift during the late Pleistocene indicates that the intensity of Deep Western Boundary Current (DWBC) might have been enhanced during the glacial period as a result of increased production of Antarctic Bottom Water (AABW).

• Journal of Veterinary Clinics
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• v.40 no.4
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• pp.243-259
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• 2023

Machine learning and deep learning (ML/DL) algorithms have been successfully applied in medical practice. However, their application in veterinary medicine is relatively limited, possibly due to a lack in the quantity and quality of relevant research. Because the potential demands for ML/DL applications in veterinary clinics are significant, it is important to note the current gaps in the literature and explore the possible directions for advancement in this field. Thus, a scoping review was conducted as a situation analysis. We developed a search strategy following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed and Embase databases were used in the initial search. The identified items were screened based on predefined inclusion and exclusion criteria. Information regarding model development, quality of validation, and model performance was extracted from the included studies. The current review found 55 studies that passed the criteria. In terms of target animals, the number of studies on industrial animals was similar to that on companion animals. Quantitative scarcity of prediction studies (n = 11, including duplications) was revealed in both industrial and non-industrial animal studies compared to diagnostic studies (n = 45, including duplications). Qualitative limitations were also identified, especially regarding validation methodologies. Considering these gaps in the literature, future studies examining the prediction and validation processes, which employ a prospective and multi-center approach, are highly recommended. Veterinary practitioners should acknowledge the current limitations in this field and adopt a receptive and critical attitude towards these new technologies to avoid their abuse.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.35D no.9
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• pp.54-61
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• 1998

A new Triple well structure is proposed for improved latch-up immunity at deep submicron CMOS device. Optimum latch-up immunity process condition is established and analyzed with varying ion implantation energy and amount of dose and also compared conventional twin well structure. Doping profile and structure are investigated using ATHENA which is process simulator, and then latch-up current is calculated using ATLAS which is device simulator. Two types of different process are affected by latch-up characteristics and shape of doping profiles. Finally, we obtained the best latch-up immunity with 2.5[mA/${\mu}{m}$] trigger current using 2.5 MeV implantation energy and 1$\times$10$^{14}$ [cm$^{-2}$ ] dose at p-well

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2012-2027
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• 2019

Nutritional disorders are one of the most common diseases of crops and they often result in significant loss of agricultural output. Moreover, the imbalance of nutrition element not only affects plant phenotype but also threaten to the health of consumers when the concentrations above the certain threshold. A number of disease identification systems have been proposed in recent years. Either the time consuming or accuracy is difficult to meet current production management requirements. Moreover, most of the systems are hard to be extended, only detect a few kinds of common diseases with great difference. In view of the limitation of current approaches, this paper studies the effects of different trace elements on crops and establishes identification system. Specifically, we analysis and acquire eleven types of tomato nutritional disorders images. After that, we explore training and prediction effects and significances of super resolution of identification model. Then, we use pre-trained enhanced deep super-resolution network (EDSR) model to pre-processing dataset. Finally, we design and implement of diagnosis system based on deep learning. And the final results show that the average accuracy is 81.11% and the predicted time less than 0.01 second. Compared to existing methods, our solution achieves a high accuracy with much less consuming time. At the same time, the diagnosis system has good performance in expansibility and portability.

• Investigative Magnetic Resonance Imaging
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• v.23 no.2
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• pp.81-99
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• 2019

Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.

• ETRI Journal
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• v.31 no.4
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• pp.429-437
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• 2009

This paper proposes a novel technique for 3D scene relighting with interactive viewpoint changes. The proposed technique is based on a deep framebuffer framework for fast relighting computation which adopts image-based techniques to provide arbitrary view-changing. In the preprocessing stage, the shading parameters required for the surface shaders, such as surface color, normal, depth, ambient/diffuse/specular coefficients, and roughness, are cached into multiple deep framebuffers generated by several caching cameras which are created in an automatic manner. When the user designs the lighting setup, the relighting renderer builds a map to connect a screen pixel for the current rendering camera to the corresponding deep framebuffer pixel and then computes illumination at each pixel with the cache values taken from the deep framebuffers. All the relighting computations except the deep framebuffer pre-computation are carried out at interactive rates by the GPU.