• Journal of Animal Science and Technology
• /
• v.64 no.5
• /
• pp.813-829
• /
• 2022

Internal and external defects of eggs should be detected to prevent cross-contamination of intact eggs by abnormal eggs during storage. Emerging detection technologies for abnormal eggs were introduced as an alternative to human inspection. The advanced technologies could rapidly detect abnormal eggs. Abnormal egg detection technologies using acoustic response, machine vision, and spectroscopy have been commercialized in the poultry industry. Non-destructive egg quality assessment methods meanwhile could preserve the value of eggs and improve detection efficiency. In order to improve detection efficiency, it is essential to select a proper algorithm for classifying the types of abnormal eggs. This review deals with the performance of the detection technologies for various types of abnormal eggs in recently published resources. In addition, the discriminant methods and detection algorithms of abnormal eggs reported in the published literature were investigated. Although the majority of the studies were conducted on a laboratory scale, the developed detection technologies for internal and external defects in eggs were technically feasible to obtain the excellent detection accuracy. To apply the developed detection technologies to the poultry industry, it is necessary to achieve the detection rates required from the industry.

• Journal of the Korean Society of Industry Convergence
• /
• v.25 no.6_3
• /
• pp.1285-1291
• /
• 2022

In this paper, we propose a low-cost, low-power embedded environment-based deep learning lightweight model for input images to recognize laundry management codes. Laundry franchise companies mainly use barcode recognition-based systems to record laundry consignee information and laundry information for laundry collection management. Conventional laundry collection management systems using barcodes require barcode printing costs, and due to barcode damage and contamination, it is necessary to improve the cost of reprinting the barcode book in its entirety of 1 billion won annually. It is also difficult to do. Recognition performance is improved by applying the VGG model with 7 layers, which is a reduced-transformation of the VGGNet model for number recognition. As a result of the numerical recognition experiment of service parts drawings, the proposed method obtained a significantly improved result over the conventional method with an F1-Score of 0.95.

• Journal of information and communication convergence engineering
• /
• v.21 no.3
• /
• pp.208-215
• /
• 2023

Deep learning techniques provide powerful solutions to several pattern-recognition problems, including Raman spectral classification. However, these networks require large amounts of labeled data to perform well. Labeled data, which are typically obtained in a laboratory, can potentially be alleviated by data augmentation. This study investigated various data augmentation techniques and applied multiple deep learning methods to Raman spectral classification. Raman spectra yield fingerprint-like information about chemical compositions, but are prone to noise when the particles of the material are small. Five augmentation models were investigated to build robust deep learning classifiers: weighted sums of spectral signals, imitated chemical backgrounds, extended multiplicative signal augmentation, and generated Gaussian and Poisson-distributed noise. We compared the performance of nine state-of-the-art convolutional neural networks with all the augmentation techniques. The LeNet5 models with background noise augmentation yielded the highest accuracy when tested on real-world Raman spectral classification at 88.33% accuracy. A class activation map of the model was generated to provide a qualitative observation of the results.

• International Journal of Computer Science & Network Security
• /
• v.23 no.9
• /
• pp.162-165
• /
• 2023

Power can be generated from either renewable or non-renewable sources. Renewable sources are liked to maintain a strategic distance from contamination emanation and rely on upon fossil energizes which is decreasing day by day. The proposed sun powered vitality transformation unit comprises of a sun oriented exhibit, Bidirectional DC-DC converter, single stage inverter and AC. The inverter changes over DC control from the PV board into AC power and offered it to the heap which is associated with the lattice. The photovoltaic sun powered vitality (PV) is the most direct approach to change over sunlight based radiation into power and depends on the photovoltaic impact. The most extreme power point following of the PV yield for all daylight conditions is a key to keep the yield control per unit cost low for fruitful PV applications. Framework associated PV frameworks dependably have an association with people in general power matrix by means of an appropriate inverter in light of the fact that a PV module conveys just dc power. This project presents the new design, Development and Performance Analysis of a Grid Connected PV Inverter. Demonstrate that the proposed framework can lessen the Energy Consumption radically from the power board and give a solid support to the Grid.

• Progress in Superconductivity and Cryogenics
• /
• v.11 no.4
• /
• pp.20-24
• /
• 2009

YBCO coated conductors (CC) on the $CeO_2$-buffered IBAD-MgO template were fabricated by metal-organic deposition (MOD) Process with Ba-trifluoroacetate and fluorine-free Y and Cu precursor materials. The precursor solution was coated on $CeO_2$-buffered IBAD MgO templates using the multiple dip-coating method, decomposed into inorganic precursors by pyrolysis up to $400^{\circ}C$ within 3 h, and finally fired at $740{\sim}800^{\circ}C$ in a reduced oxygen atmosphere. Microstructure, texture, and superconducting properties of YBCO films were found highly sensitive to both the firing temperature and time. The high critical current density ($J_C$) of $1.15\;MA/cm^2$ at 77.3K in the self-field could be obtained from $1\;{\mu}m$ thick YBCO CC, fired at $740^{\circ}C$ for 3.5 h, implying that high performance YBCO CC is producible on IBAD MgO template. Further enhancement of $J_C$ values is expected by improving the in-plane texture of $CeO_2$-buffer layer and avoiding the metal substrate contamination.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.1
• /
• pp.261-279
• /
• 2023

In this article, the uplink achievable rate is investigated for massive multiple-input multiple-output (MIMO) under correlated Ricean fading channel, where each base station (BS) and user are both deployed multiple antennas. Considering the availability of prior knowledge at BS, two different channel estimation approaches are adopted with and without prior knowledge. Based on these channel estimations, a two-layer decoding scheme is adopted with maximum ratio precoding as the first layer decoder and optimal second layer precoding in the second layer. Based on two aforementioned channel estimations and two-layer decoding scheme, the exact closed form expressions for uplink achievable rates are computed with and without prior knowledge, respectively. These derived expressions enable us to analyze the impacts of line-of-sight (LoS) component, two-layer decoding, data transmit power, pilot contamination, and spatially correlated Ricean fading. Then, numerical results illustrate that the system with spatially correlated Ricean fading channel is superior in terms of uplink achievable rate. Besides, it reveals that compared with the single-layer decoding, the two-layer decoding scheme can significantly improve the uplink achievable rate performance.

• Journal of Veterinary Science
• /
• v.23 no.2
• /
• pp.23.1-23.15
• /
• 2022

Background: Antibacterial agents play important roles in the treatment of bacterial infections. However, the development of antimicrobial resistance (AMR) and carry-over of substances into the environment are several problems arising during oral treatment of bacterial infections. We assessed AMR development in commensal Escherichia coli (E. coli) in enrofloxacin treated and untreated animals. In addition, we examined fluoroquinolone in the plasma and urine of treated and untreated animals, and in sedimentation dust and aerosol. Methods: In each trial, six pigs were treated with enrofloxacin via powder, granulate or pellet forms in two time periods (days 1-5 and 22-26). Four pigs served as untreated controls. The minimum inhibitory concentration (MIC) was determined to evaluate AMR development. Analysis of enro- and ciprofloxacin was performed with high performance liquid chromatography. Results: Non-wildtype E. coli (MIC > 0.125 ㎍/mL) was detected in the pellet treated group after the first treatment period, whereas in the other groups, non-wildtype isolates were found after the second treatment period. E. coli with MIC > 4 ㎍/mL was found in only the pellet trial. Untreated animals showed similar susceptibility shifts several days later. Bioavailability differed among the treatment forms (granulate > pellet > powder). Enro- and ciprofloxacin were detected in aerosols and sedimentation dust (granulate, powder > pellet). Conclusions: This study indicates that the kind of the oral dosage form of antibiotics affects environmental contamination and AMR development in commensal E. coli in treated and untreated pigs.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.115-115
• /
• 2020

Water network partitioning (WNP) is an initiative technique to divide the original water distribution network (WDN) into several sub-networks with only sparse connections between them called, District Metered Areas (DMAs). Operating and managing (O&M) WDN through DMAs is bringing many advantages, such as quantification and detection of water leakage, uniform pressure management, isolation from chemical contamination. The research of WNP recently has been highlighted by applying different methods for dividing a network into a specified number of DMAs. However, it is an open question on how to determine the optimal number of DMAs for a given network. In this study, we present a method to divide an original WDN into DMAs (called Clustering) based on community structure algorithm for auto-creation of suitable DMAs. To that aim, many hydraulic properties are taken into consideration to form the appropriate DMAs, in which each DMA is controlled as uniform as possible in terms of pressure, elevation, and water demand. In a second phase, called Sectorization, the flow meters and control valves are optimally placed to divide the DMAs, while minimizing the pressure reduction. To comprehensively evaluate the WNP performance and determine optimal number of DMAs for given WDN, we apply the framework of multiple-criteria decision analysis. The proposed method is demonstrated using a real-life benchmark network and obtained permissible results. The approach is a decision-support scheme for water utilities to make optimal decisions when designing the DMAs of their WDNs.

• Smart Media Journal
• /
• v.11 no.7
• /
• pp.94-103
• /
• 2022

Weeds bring disadvantages to crops since they can damage them, and a clean treatment with less pollution and contamination should be developed. Artificial intelligence gives new hope to agriculture to achieve smart farming. This study delivers an automated weeds growth point detection using deep learning. This study proposes a combination of semantic graphics for generating data annotation and U-Net with pre-trained deep learning as a backbone for locating the growth point of the weeds on the given field scene. The dataset was collected from an actual field. We measured the intersection over union, f1-score, precision, and recall to evaluate our method. Moreover, Mobilenet V2 was chosen as the backbone and compared with Resnet 34. The results showed that the proposed method was accurate enough to detect the growth point and handle the brightness variation. The best performance was achieved by Mobilenet V2 as a backbone with IoU 96.81%, precision 97.77%, recall 98.97%, and f1-score 97.30%.

• Tribology and Lubricants
• /
• v.39 no.5
• /
• pp.212-215
• /
• 2023

Diaphragm compressors play a crucial role in safely compressing large volumes of high-purity hydrogen gas without contamination or leakage, thereby ensuring quality and reliability. Diaphragm compressors use a thin, flat, triple-layered diaphragm plate that is subjected to repetitive piston pressure for compression. They are usually made of metallic materials such as stainless steel or Inconel owing to their high-pressure resistance. However, since they are consumable components, they fail due to fatigue from repetitive pressure and vibration stress. This study aims to evaluate the scratch characteristics of diaphragms in operational environments by conducting tests on three different samples: Inconel 718, AISI 301, and Teflon-coated AISI 301. The Inconel 718 sample underwent a polishing process, the AISI 301 sample used raw material, and the Teflon coating was applied to the AISI 301 substrate at a thickness of 50 ㎛. To assess the scratch resistance, reciprocating motion friction tests were performed using a tribometer, utilizing 220 and 2000 grit sandpapers as the counter materials. The results of the friction tests suggested that the Teflon-coated sample exhibited the lowest initial friction coefficient and consistently maintained the lowest average friction coefficient (0.13 and 0.11 with 220 and 2000 grit, respectively) throughout the test. Moreover, the Teflon-coated diaphragm showed minimal wear patterns, indicating superior scratch resistance than the Inconel 718 and AISI 301 samples. These findings suggest that Teflon coatings may offer an effective solution for enhancing scratch resistance in diaphragms, thereby improving compressor performance in high-pressure hydrogen applications.