• Journal of Multimedia Information System
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• v.9 no.2
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• pp.145-154
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• 2022

The authors are making a prototype flexible board of a radio-frequency transmitter for measuring an electromyogram (EMG) of a flying moth and plan to apply for an experimental station license from the Ministry of Internal Affairs and Communications of Japan in the summer of 2022. The goal is to create a continuous low-dose exposure standard that incorporates scientific and physiological functional assessments to replace the current standard based on lethal dose 50. This paper describes the technical evaluation of the hardware. The signal of a bipolar EMG electrode is amplified by an operational amplifier. This potential is added to a voltage-controlled crystal oscillator (27 MHz, bandwidth: 4 kHz), frequency-converted, and transmitted from an antenna about 10 cm long (diameter: 0.03 mm). The power source is a 1.55-V wristwatch battery that has a total weight of about 0.3 g (one dry battery and analog circuit) and an expected operating time of 20 minutes. The output power is -7 dBm and the effective isotropic radiated power is -40 dBm. The signal is received by a dual-whip antenna (2.15 dBi) at a distance of about 100 m from the moth. The link margin of the communication circuit is above 30 dB within 100 m. The concepts of this hardware and the measurement data are presented in this paper. This will be the first biological data transmission from a moth with an official license. In future, this telemetry system will improve the detection of physiological abnormalities of moths.

• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.3
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• pp.201-207
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• 2022

Fescues, which are widely cultivated as grasses and forages around the world, are often naturally infected with the endophyte, Epichloë. This fungus, transmitted through seeds, imparts resistance to drying and herbivorous insects in its host without causing any external damage, thereby contributing to the adaptation of the host to the environment and maintaining a symbiosis. However, some endophytes, such as E. coenophialum synthesize ergovaline or lolitrem B, which accumulate in the plant and impart anti-mammalian properties. For example, when livestock consume excessive amounts of grass containing toxic endophytes, problems associated with neuromuscular abnormalities, such as convulsions, paralysis, high fever, decreased milk production, reproductive disorders, and even death, can occur. Therefore, pre-inoculation with non-toxic endogenous fungi or management with endophyte-free grass is important in preventing damage to livestock and producing high-quality forage. To date, the diagnosis of endophytes has been mainly performed by observation under a microscope following staining, or by performing an immune blot assay using a monoclonal antibody. Recently, the polymerase chain reaction (PCR)-based molecular diagnostic method is gaining importance in the fields of agriculture, livestock, and healthcare given the method's advantages. These include faster results, with greater accuracy and sensitivity than those obtained using conventional diagnostic methods. For the diagnosis of endophytes, the nested PCR method is the only available option developed; however, it is limited by the fact that the level of toxic alkaloid synthesis cannot be estimated. Therefore, in this study, we aimed to develop a triplex real-time PCR diagnostic method that can determine the presence or absence of endophyte infection using DNA extracted from seeds within 1 h, while simultaneously detecting easD and LtmC genes, which are related to toxic alkaloid synthesis. This new method was then also applied to real field samples.

• Clinical Pain
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• v.19 no.2
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• pp.90-96
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• 2020

Objective: To investigate the diagnostic accuracy of two physical examinations (straight leg raise [SLR] and Bragard test) and electromyography (EMG) in patients with lumbosacral monoradiculopathy in acute and chronic state on confirmation of different diagnostic criteria (MRI vs MRI and diagnostic selective nerve root block [DSNRB]). Method: We identified 297 participants retrospectively from the departmental database. MRI evidence of L5 or S1 nerve root compression and a positive result in diagnostic SNRB served as reference standards. They were divided into two groups by the symptom duration: lasting more than 12 weeks in the chronic group and less than 12 weeks in the acute group. The diagnostic value of clinical tests and EMG were compared. Results: The clinical tests (SLR and Bragard test) done in acute stage on detection by MRI and DSNRB had the highest sensitivity (68%) compared to the chronic stage (63%), but sensitivity was low (57%) on confirmation of MRI alone. However, there was no significant difference on sensitivity and specificity of EMG regardless of reference standards and symptom duration. Electromyography was a significant predictor of neuropathic abnormalities on both acute (OR, 6.3; 95% CI, 2.4 to 16.7; p<0.01) and chronic (OR, 6.8; 95% CI, 2.9 to 16.3; p<0.01). Conclusion: In general, individual physical tests are easy to do and a combination of those tests could be a sensitive indicator of L5 or S1 radiculopathy. Furthermore, the use of provocation tests could provide useful information, especially in proceeding therapeutic selective nerve root block.

• Journal of the korean academy of Pediatric Dentistry
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• v.50 no.2
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• pp.142-154
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• 2023

The canine tooth is important both functionally and aesthetically, being positioned between the anterior and posterior teeth. The upper canine has the longest eruption path, forming in the deepest part of the maxillary bone and often experiencing eruption disorders, leading to significant aesthetic and functional issues. Early detection and management of canine impaction are crucial in pediatric dentistry, which focuses on tooth growth and eruption. The prevalence of maxillary canine impaction ranges from 1.1% to 3.0%. In Western populations, palatal impaction is twice as common as labial impaction, while some Korean studies report more labial impaction. Maxillary canine impaction occurs more frequently in women and is associated with various factors such as structural obstacles, pathological conditions in surrounding tissues, developmental abnormalities, and genetic factors. Labial displaced canines are linked to narrow maxillary intercanine width, total dental crowding, and skeletal Class III malocclusion. Maxillary palatal displaced canine impaction is more prevalent in patients with familial agenesis of lateral incisors or conical supernumerary lateral incisors. Understanding these factors aids in early diagnosis and appropriate intervention for canine tooth impaction, ensuring optimal oral health and aesthetics.

• Korean Journal of Veterinary Research
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• v.64 no.1
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• pp.7.1-7.6
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• 2024

Bird species, particularly poultry and other bird types, including domestic pigeons, are susceptible to fowl pox, a contagious viral disease. The main goal of this study was to validate clinical avipoxvirus diagnoses using molecular analytical methods. The essential components of the investigation were the clinical signs, visible abnormalities, histological changes, and polymerase chain reaction analysis. Twenty out of 120 pigeons had clinical symptoms, which included yellowish crust or nodules near the feet, eyes, and beak. An erosive epidermal lesion and an epidermal acanthotic papular lesion with basal vacuolation were maculopapular evidence associated with significant epidermal hyperkeratosis, as confirmed by histological analysis. In addition, the results showed keratinocyte necrosis beneath the hyperkeratotic epidermal layer, together with superficial and deep dermal perivascular lymphocytic infiltration. In addition, the P4b core protein gene underwent phylogenetic analysis. The sequence analysis results indicated a high degree of similarity across the local strains, with just minor variations observed. Five sample sequences were selected and submitted to the NCBI database. These sequences were identified as OR187728, OR187729, OR187730, OR187731, and OR187732. All the various strains in this research may be classified under clade A of the chicken pox virus phylogenetic classification. This study presents the first description and characterization of pox virus infections in domestic pigeons inside the Basrah governorate.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.109-116
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• 2020

With the advent of high-speed railways, railways are one of the most frequently used means of transportation at home and abroad. In addition, in terms of environment, carbon dioxide emissions are lower and energy efficiency is higher than other transportation. As the interest in railways increases, the issue related to railway safety is one of the important concerns. Among them, visual abnormalities occur when various obstacles such as animals and people suddenly appear in front of the railroad. To prevent these accidents, detecting rail tracks is one of the areas that must basically be detected. Images can be collected through cameras installed on railways, and the method of detecting railway rails has a traditional method and a method using deep learning algorithm. The traditional method is difficult to detect accurately due to the various noise around the rail, and using the deep learning algorithm, it can detect accurately, and it combines the two algorithms to detect the exact rail. The proposed algorithm determines the accuracy of railway rail detection based on the data collected.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• The Journal of Bigdata
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• v.7 no.1
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• pp.125-137
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• 2022

The transportation industry is one of the important industries due to the geographical requirements surrounded by the sea on three sides of Korea and the problem of resource poverty, which relies on imports for most of its resource consumption. Among them, the proportion of the shipping industry is large enough to account for most of the transportation industry, and maintenance in the shipping industry is also important in improving the operational efficiency and reducing costs of ships. However, currently, inspections are conducted every certain period of time for maintenance of ships, resulting in time and cost, and the cause is not properly identified. Therefore, in this study, the proposed methodology, LSTM-AutoEncoder, is used to detect abnormalities that may cause ship failure by considering the time of actual ship operation data. In addition, clustering is performed through clustering, and the potential causes of ship main engine failure are identified by grouping outlier by factor. This enables faster monitoring of various information on the ship and identifies the degree of abnormality. In addition, the current ship's fault monitoring system will be equipped with a concrete alarm point setting and a fault diagnosis system, and it will be able to help find the maintenance time.

• Clinical and Experimental Pediatrics
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• v.52 no.2
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• pp.199-204
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• 2009

Purpose : Seizure associated with fever may indicate the presence of underlying inherited metabolic diseases. The present study was performed to investigate the presence of underlying metabolic diseases in patients with complex febrile seizures, using analyses of urine organic acids. Method : We retrospectively analyzed and compared the results of urine organic acid analysis with routine laboratory findings in 278 patients referred for complex febrile seizure. Results : Of 278 patients, 132 had no abnormal laboratory findings, and 146 patients had at least one of the following abnormal laboratory findings: acidosis (n=58), hyperammonemia (n=55), hypoglycemia (n=21), ketosis (n=12). Twenty-six (19.7 %) of the 132 patients with no abnormal findings and 104 (71.2%) of the 146 patients with statistically significant abnormalities showed abnormalities on the organic acid analysis (P<0.05). Mitochondrial respiratory chain disorders (n=23) were the most common diseases found in the normal routine laboratory group, followed by PDH deficiency (n=2) and ketolytic defect (n=1). In the abnormal routine laboratory group, mitochondrial respiratory chain disorder (n=29) was the most common disease, followed by ketolytic defects (n=27), PDH deficiency (n=9), glutaric aciduria type II (n=9), 3-methylglutaconic aciduria type III (n=6), biotinidase deficiency (n=5), propionic acidemia (n=4), methylmalonic acidemia (n=2), 3-hydroxyisobutyric aciduria (n=2), orotic aciduria (n=2), fatty acid oxidation disorders (n=2), 2-methylbranched chain acyl CoA dehydrogenase deficiency (n=2), 3-methylglutaconic aciduria type I (n=1), maple syrup urine disease (n=1), isovaleric acidemia (n=1), HMG-CoA lyase deficiency (n=1), L-2-hydroxyglutaric aciduria (n=1), and pyruvate carboxylase deficiency (n=1). Conclusion : These findings suggest that urine organic acid analysis should be performed in all patients with complex febrile seizure and other risk factors for early detection of inherited metabolic diseases.