• Journal of The Korean Society of Agricultural Engineers
• /
• v.63 no.1
• /
• pp.37-48
• /
• 2021

The automatic-spreader of bedding materials was developed to reduce labor cost, and to achieve successful biosecurity in duck houses. Algorithm of the device was designed to realize a concept of the automatic unmanned operation including entire processes such as loading and spreading of the bedding materials. From the field measurement, the relationship between the expected water content reduction and weight of bedding materials per unit area according to the operation condition of the devices was induced. In the case of the measurement of particulate matters during the process of spreading works, the results of using both conventional manual-spreader and automatic-spreader were exceeded the allowable limit of inhalable and respirable dust, respectively. But, workers using automatic-spreader could be free from harmful aero-condition because they did not stay inside the facility during the spreading works. In addition, from the Duck hepatitis virus PCR test, influence on pulmonary tissue of the duck was not found. It could be expected that the development of the automatic-spreader of bedding materials for duck house can contribute to the advancement of duck breeding facilities.

• Korean Journal of Agricultural Science
• /
• v.49 no.3
• /
• pp.483-493
• /
• 2022

It is important to improve the efficiency of plant breeding and crop yield to fulfill increasing food demands. In plant phenotyping studies, the capability to correlate morphological traits such as plant height, stem diameter, leaf length, leaf width, leaf angle and size of panicle of the plants has an important role. However, manual phenotyping of plants is prone to human errors and is labor intensive and time-consuming. Hence, it is important to develop techniques that measure plant phenotypic traits accurately and rapidly. The aim of this study was to determine the feasibility of point cloud data based on a 3D light detection and ranging (LiDAR) system for plant phenotyping. The obtained results were then verified through manually acquired data from the sorghum samples. This study measured the plant height, plant crown diameter and the panicle height and diameter. The R² of each trait was 0.83, 0.94, 0.90, and 0.90, and the root mean square error (RMSE) was 6.8 cm, 1.82 cm, 5.7 mm, and 7.8 mm, respectively. The results showed good correlation between the point cloud data and manually acquired data for plant phenotyping. The results indicate that the 3D LiDAR system has potential to measure the phenotypes of sorghum in a rapid and accurate way.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.117-127
• /
• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• International Journal of Computer Science & Network Security
• /
• v.22 no.11
• /
• pp.213-221
• /
• 2022

Intelligent Character Recognition System for Account Payable (ICRS AP) Automation represents the process of capturing text from scanned invoices and extracting the key fields from invoices and storing the captured fields into properly structured document format. ICRS plays a very critical role in invoice data streamlining, we are interested in data like Vendor Name, Purchase Order Number, Due Date, Total Amount, Payee Name, etc. As companies attempt to cut costs and upgrade their processes, accounts payable (A/P) is an example of a paper-intensive procedure. Invoice processing is a possible candidate for digitization. Most of the companies dealing with an enormous number of invoices, these manual invoice matching procedures start to show their limitations. Receiving a paper invoice and matching it to a purchase order (PO) and general ledger (GL) code can be difficult for businesses. Lack of automation leads to more serious company issues such as accruals for financial close, excessive labor costs, and a lack of insight into corporate expenditures. The proposed system offers tighter control on their invoice processing to make a better and more appropriate decision. AP automation solutions provide tighter controls, quicker clearances, smart payments, and real-time access to transactional data, allowing financial managers to make better and wiser decisions for the bottom line of their organizations. An Intelligent Character Recognition System for AP Automation is a process of extricating fields like Vendor Name, Purchase Order Number, Due Date, Total Amount, Payee Name, etc. based on their x-axis and y-axis position coordinates.

• Smart Structures and Systems
• /
• v.31 no.4
• /
• pp.325-334
• /
• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• Journal of Drive and Control
• /
• v.20 no.3
• /
• pp.35-41
• /
• 2023

Radish and Chinese cabbage are the most produced and consumed vegetables in Korea. The mechanization of harvesting operations is necessary to minimize the need for manual labor. This study to develop and evaluate the performance of a multi-purpose driving platform that can apply modular Radish and Chinese cabbage harvesting devices. The multi-purpose driving platform consisted of driving, device control, engine, hydraulic, harvesting, conveying, and loading part. Radish and Chinese cabbage harvesting conducted using the multi-purpose driving platform each harvesting module. The performance of the multi-purpose driving platform was evaluated the field efficiency and loss rate. The total Radish harvesting operation time 34.3 min., including 28.8 min., of harvesting time, 1.9 min., of turning time, and 3.6 min., of replacement time of bulk bag. During Radish harvesting, the field efficiency and average loss rate of the multi-purpose driving platform were 2.0 hr/10a and 3.1 %. Chinese cabbage harvesting operation 49.3 min., including 26.6 min., of harvesting time, 4.6 min., of turning time, and 18.1 min., of replacement time of bulk bag. During Chinese cabbage harvesting, the field efficiency and average loss rate of the multi-purpose driving platform 2.1 hr/10a and 0.1 %. Performance evaluation of the multi-purpose driving platform that harvesting work was possible by installing Radish and Chinese cabbage harvest modules. Performance analysis through harvest performance evaluation in various Radish and Chinese cabbage cultivation environments is necessary.

• Journal of Information Technology Services
• /
• v.22 no.4
• /
• pp.97-110
• /
• 2023

In this study, we propose a technique to automatically generate transfer documents using sensor data from livestock manure transfer systems. The research involves analyzing sensor data and applying machine learning techniques to derive optimized outcomes for livestock manure transfer documents. By comparing and contrasting with existing documents, we present a method for automatic document generation. Specifically, we propose the utilization of Gradient Boosting, a machine learning algorithm. The objective of this research is to enhance the efficiency of livestock manure and liquid byproduct management. Currently, stakeholders including producers, transporters, and processors manually input data into the livestock manure transfer management system during the disposal of manure and liquid byproducts. This manual process consumes additional labor, leads to data inconsistency, and complicates the management of distribution and treatment. Therefore, the aim of this study is to leverage data to automatically generate transfer documents, thereby increasing the efficiency of livestock manure and liquid byproduct management. By utilizing sensor data from livestock manure and liquid byproduct transport vehicles and employing machine learning algorithms, we establish a system that automates the validation of transfer documents, reducing the burden on producers, transporters, and processors. This efficient management system is anticipated to create a transparent environment for the distribution and treatment of livestock manure and liquid byproducts.

• Clinics in Shoulder and Elbow
• /
• v.26 no.2
• /
• pp.156-161
• /
• 2023

Background: We analyzed association between viewing two-dimensional computed tomography (2D CT) images in addition to radiographs with radial head treatment recommendations after accounting for patient and surgeon factors in a survey-based experiment. Methods: One hundred and fifty-four surgeons reviewed 15 patient scenarios with terrible triad fracture dislocations of the elbow. Surgeons were randomized to view either radiographs only or radiographs and 2D CT images. The scenarios randomized patient age, hand dominance, and occupation. For each scenario, surgeons were asked if they would recommend fixation or arthroplasty of the radial head. Multi-level logistic regression analysis identified variables associated with radial head treatment recommendations. Results: Reviewing 2D CT images in addition to radiographs had no statistical association with treatment recommendations. A higher likelihood of recommending prosthetic arthroplasty was associated with older patient age, patient occupation not requiring manual labor, surgeon practice location in the United States, practicing for five years or less, and the subspecialties "trauma" and "shoulder and elbow." Conclusions: The results of this study suggest that in terrible triad injuries, the imaging appearance of radial head fractures has no measurable influence on treatment recommendations. Personal surgeon factors and patient demographic characteristics may have a larger role in surgical decision making. Level of evidence: Level III, therapeutic case-control study.

• Clinics in Shoulder and Elbow
• /
• v.25 no.4
• /
• pp.257-264
• /
• 2022

Background: Chronic subscapularis tendon tear (SBT) is a degenerative disease and a common pathologic cause of shoulder pain. Several potential risk factors for chronic SBT have been reported. Although metabolic abnormalities are common risk factors for degenerative disease, their potential etiological roles in chronic SBT remains unclear. The purpose of this study was to investigate potential risk factors for chronic SBT, with particular attention to metabolic factors. Methods: This study evaluated single shoulders of 939 rural residents. Each subject undertook a questionnaire, physical examinations, blood tests, and simple radiographs and magnetic resonance imaging (MRI) evaluations of bilateral shoulders. Subscapularis tendon integrity was determined by MRI findings based on the thickness of the involved tendons. The association strengths of demographic, physical, social, and radiologic factors, comorbidities, severity of rotator cuff tear (RCT), and serologic parameters for SBT were evaluated using logistic regression analyses. The significance of those analyses was set at p<0.05. Results: The prevalence of SBT was 32.2% (302/939). The prevalence of partial- and full-thickness tears was 23.5% (221/939) and 8.6% (81/939), respectively. The prevalence of isolated SBT was 20.2% (190/939), SBT combined with supraspinatus or infraspinatus tendon tear was 11.9% (112/939). In multivariable logistic regression analysis, dominant side involvement (p<0.001), manual labor (p=0.002), diabetes (p<0.001), metabolic syndrome (p<0.001), retraction degree of Patte tendon (p<0.001), posterosuperior RCT (p=0.010), and biceps tendon injury (p<0.001) were significantly associated with SBT. Conclusions: Metabolic syndrome is a potential risk factor for SBT, as are these factors: overuse activity, diabetes, posterosuperior RCT, increased retraction of posterosuperior rotator cuff tendon, and biceps tendon injury.

• Journal of the Korea Institute of Building Construction
• /
• v.23 no.3
• /
• pp.273-284
• /
• 2023

This study is centered on the combined use of LiDAR(Light Detection and Ranging) and AR(Augmented Reality) technologies during vertical and horizontal frame measurements in construction projects. The intention is to enhance the quality control procedure, elevate accuracy, and curtail manual labor along with time expenditure. Present methods for accuracy inspection in frame construction often grapple with reliability concerns due to subjective interpretation and the scope for human error. This research recommends the application of LiDAR and AR technologies to counter these issues and augment the efficiency of the inspection process, along with facilitating the dissemination of results. The suggested technique involves the collection of 3D point cloud data of the frame utilizing LiDAR and leveraging this data for checks on construction accuracy. Furthermore, the inspection outcomes are fed into a BIM (Building Information Modeling) model, and the results are visualized via AR. Upon juxtaposing this methodology with the current approach, it is evident that it offers benefits in terms of objective inspection, speed, precise result sharing, and potential enhancements to the overall quality and productivity of construction projects.