• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.39 no.4
• /
• pp.347-357
• /
• 2003

This paper describes on the suppression of sea clutter on marine radar display using a cell-averaging CFAR(constant false alarm rate) technique, and on the analysis of radar echo signal data in relation to the estimation of ARPA functions and the detection of the shadow effect in clutter returns. The echo signal was measured using a X -band radar, that is located on the Pukyong National University, with a horizontal beamwidth of $$3.9^{\circ}$$, a vertical beamwidth of $20^{\circ}$, pulsewidth of $0.8 {\mu}s$ and a transmitted peak power of 4 ㎾ The suppression performance of sea clutter was investigated for the probability of false alarm between $l0-^0.25;and; 10^-1.0$. Also the performance of cell averaging CFAR was compared with that of ideal fixed threshold. The motion vectors and trajectory of ships was extracted and the shadow effect in clutter returns was analyzed. The results obtained are summarized as follows；1. The ARPA plotting results and motion vectors for acquired targets extracted by analyzing the echo signal data were displayed on the PC based radar system and the continuous trajectory of ships was tracked in real time. 2. To suppress the sea clutter under noisy environment, a cell averaging CFAR processor having total CFAR window of 47 samples(20+20 reference cells, 3+3 guard cells and the cell under test) was designed. On a particular data set acquired at Suyong Man, Busan, Korea, when the probability of false alarm applied to the designed cell averaging CFAR processor was 10$^{-0}$.75/ the suppression performance of radar clutter was significantly improved. The results obtained suggest that the designed cell averaging CFAR processor was very effective in uniform clutter environments. 3. It is concluded that the cell averaging CF AR may be able to give a considerable improvement in suppression performance of uniform sea clutter compared to the ideal fixed threshold. 4. The effective height of target, that was estimated by analyzing the shadow effect in clutter returns for a number of range bins behind the target as seen from the radar antenna, was approximately 1.2 m and the information for this height can be used to extract the shape parameter of tracked target..

• Korean Journal of Environmental Biology
• /
• v.31 no.4
• /
• pp.275-287
• /
• 2013

The Japanese anchovy Engraulis japonicus is a widespread species in the western North Pacific and major fishery resource. To understand the spatio-temporal variation of anchovy prey items in the coastal waters of southern Korea, the stomach contents of anchovy and the structure of the zooplankton community were analysed at three sites (Jindo, Yeosu and Tong-yeong) from July 2011 to February 2012. The main prey items in Yeosu and Jindo were cyprid stage of barnacle (>35%) and copepod Calanus sinicus (>22%) in July, respectively, while, predominant ones in Tongyeong were small copepods, Paracalanus parvus s.l. (41%) and Corycaeus affinis (22%). During this period, the dominant zooplankton were cladoceran Evadne tergestina (39%) in Yeosu, small copepod, P. parvus s.l. (28%) in Jindo and cladoceran E. tergestina (14%) in Tongyeong. The dominant prey items were barnacle larvae and copepods in summer, phytoplankton and Pseudodiaptomus marinus in autumn and P. parvus s.l. and cold water copepod, Centropages abdominalis in winter. Anchovy prefer the prey item C. sinicus (3%) over E. tergestina (39%), which was a dominant species in the catching site in summer. P. marinus (0.5%) and C. abdominalis (0.9%) were preferred over P. parvus s.l. (30%, 21%) in autumn and winter, respectively. Prey items varied with area and season in the coastal waters of southern Korea. These results suggest that the prey selectivity of anchovy showed high flexibility and adaptability in the study waters.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.33 no.6
• /
• pp.265-274
• /
• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1463-1478
• /
• 2022

Maritime transport accounts for 99.7% of the exports and imports of the Republic of Korea; therefore, developing a vessel monitoring system for efficient operation is of significant interest. Several studies have focused on tracking and monitoring vessel movements based on automatic identification system (AIS) data; however, ships without AIS have limited monitoring and tracking ability. High-resolution optical satellite images can provide the missing layer of information in AIS-based monitoring systems because they can identify non-AIS vessels and small ships over a wide range. Therefore, it is necessary to investigate vessel monitoring and small vessel classification systems using high-resolution optical satellite images. This study examined the possibility of developing ship monitoring systems using Compact Advanced Satellite 500-1 (CAS500-1) satellite images by first training a deep learning model using satellite image data and then performing detection in other images. To determine the effectiveness of the proposed method, the learning data was acquired from ships in the Yellow Sea and its major ports, and the detection model was established using the You Only Look Once (YOLO) algorithm. The ship detection performance was evaluated for a domestic and an international port. The results obtained using the detection model in ships in the anchorage and berth areas were compared with the ship classification information obtained using AIS, and an accuracy of 85.5% and 70% was achieved using domestic and international classification models, respectively. The results indicate that high-resolution satellite images can be used in mooring ships for vessel monitoring. The developed approach can potentially be used in vessel tracking and monitoring systems at major ports around the world if the accuracy of the detection model is improved through continuous learning data construction.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.25 no.3
• /
• pp.100-117
• /
• 2022

This study examines the spatio-temporal characteristics of the macrobenthic community in the coastal areas of South Korea for the past six years(2015-2020). The relationship between the number of individuals of macrobenthic species and the benthic environments were investigated using data collected at a total of 154 stations located in the West (70), the South (61), and the East Seas (23), except for the Jeju Sea. We examined the benthic environmental characteristics such as water depth, sediment, grain size, ignition loss, and total organic carbon. A total of 1,614 macrobenthic species were found in the coastal area, with a mean density of 0.62 ind./m² by station. The mean density was relatively high in the spring and summer seasons (May to August) with more than 450 species. The most dominant species belong to Polychaetes and the top five of them accounted for more than 20% of the total number of populations. The top five species were Heteromastus filiformis, Scoletoma longifolia, Sigambra tentaculata, Sternaspis scutata, and Notomastus latericeus. Cluster analysis was performed on the top five dominant species. The stations were clustered into three groups with similar locations on the West, South, and East Sea. Cluster 1 and 3 represent Heteromastus filiformis (44% each), but cluster 2 represents Scoletoma longifolia (66%). Each cluster has different benthic environmental characteristics, especially in the sediment's sand (31.0%, 51.9%) and clay (15.9%, 9.7%) contents.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.12 no.2
• /
• pp.112-120
• /
• 2007

This paper reviews the use of parasites as 'biological tags' for studying stock analysis of salmonid fishes. Numerous definitions of stock concepts exist, but most of them essentially define a group of fish as having similar biological characteristics and being self-reproducing as stocks. It is important to manage fish stocks for human consumption and sustainable production and especially for salmonid fishes. Because these fry are considered as each country's property, it is necessary to identify and discriminate each fish stock in the open sea. Methods of separating fish stocks are very diverse. Artificial tags, parasites, otoliths scales and genetic characters have been used for stock analysis and each method has advantages and disadvantages. Of these parasites can be good biological tags because they are applied by nature at no cost. Parasites can be infected with susceptible host fishes when they enter into certain areas. Then if they move to the outside and are caught researchers can infer that the fish had been in the endemic area for a period of time during their life. Hence the host fish can be considered as naturally 'tagged' by parasites. However, if they do not pass the parasites-endemic. area, they will harbour no parasites. Therefore, researchers can discriminate each fish stocks and trace their migration routes with these biological tags. In this paper, several examples on the use of parasites as biological tags for studying salmonids, as well as other species, are listed. The advantages and limitations of parasites as biological tags are also discussed. Chum salmon (Oncorhynchus keta), the main salmonid species migrating to Korea, is distributed all around the North Pacific. Korean chum salmon are generally thought to move to the Sea of Okhotsk, the western North Pacific and the Bering Sea. However, there is no clear information on the distribution and migration pathways of Korean chum salmon, and no markers exist for separating them from others yet. Recent Korean chum salmon stock analysis including parasites information are mentioned.

• The Journal of the Acoustical Society of Korea
• /
• v.43 no.1
• /
• pp.9-18
• /
• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• Korean Journal of Soil Science and Fertilizer
• /
• v.43 no.3
• /
• pp.268-274
• /
• 2010

The properties of upland soils are much more dependent upon topography than those of paddy soils, and they give us very useful information to manage the upland fields. Therefore, we investigated the selected physical and chemical properties of upland soils at 84 and 150 topographic sampling sites, respectively. The topographic sites included 34.7% of local valley and fans, 18.7% of hilly and mountains, 20.0% of mountain foot slopes, 14.0% of alluvial plains, 8.0% of diluvium, and 4.6% of fluvio-marine deposits. Based on the investigation, soil textures in Jeonbuk upland fields were mostly sandy loam, sandy clay loam, clay loam, and clay soils, especially sandy clay loam soils were evenly distributed in all of the topographic sites. Soil slopes in the sites ranged from 0 to 15%, which showed an optimal condition for farm land. Soil bulk density and compaction values were from 1.19 to 1.24 g $cm^{-3}$ and from 12.1 to 13.9 mm, respectively. As comparing with the optimal conditions of soil chemical properties for upland soils proposed by National Institute of Agricultural Science and Technology, Korea, 37%, 42.7%, 93.0% of the sites were within optimum levels with soil pH, content of soil organic matter, and electrical conductivity, respectively. However, 64.0%, 47.3%, 48.7%, and 42.7% of the upland soils contained excess levels of exchangeable K, Ca, and Mg, and available phosphorus, respectively. In addition, the contents of heavy metals, As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, in the Jeonbuk upland soils were much less than threshold levels.

• Journal of Life Science
• /
• v.26 no.11
• /
• pp.1259-1268
• /
• 2016

In this study, we investigated antimicrobial peptide from the acidified muscle extract of Mytilus coruscus, which mostly inhabits China, Japan, and Korea, to develop a natural product-derived antibiotics substitution in terms of its abuse and restriction. Antimicrobial peptide was purified by $C_{18}$ reversed-phase high-performance liquid chromatography and was detected as having a molecular mass of 6,701 Da by MALDI-TOF/MS. The N-terminal amino acid sequence of the purified peak was obtained from edman degradation, and 20 identified residues shown 100% identity with the N-terminus region of sperm-specific protein and protamine-like PL-II/PL-IV precursor of Mytilus californianus. We also identified 60 open-reading frame (ORF) encoding amino acids with 183 bp of purified peptide based on the obtained amino acid residues. The amino acid sequence of ORF showed 100% and the nucleotide sequence revealed 97.2% identity with the protamine-like PL-II/PL-IV precursor of Mytilus californianus. Synthesized antimicrobial peptide showed antimicrobial activity against gram-positive bacteria, including Bacillus cereus (minimal effective concentration [MEC], $20.8{\mu}g/ml$), Bacillus subtilis (MEC, $0.2{\mu}g/ml$), Streptococcus mutans (MEC, $0.2{\mu}g/ml$), gram-negative bacteria including Pseudomonas aeruginosa (MEC, $5.7{\mu}g/ml$), Escherichia coli (MEC, $2.6{\mu}g/ml$) and fungi, Candida albicans (MEC, $56.3{\mu}g/ml$). In addition, synthesized peptide showed stable activities under heat and salt conditions against gram-positive and gram-negative bacteria, but was inhibited by salt against only C. albicans. With these results, isolated peptide from M. coruscus could be an alternative agent to antibiotics for defending against pathogenic microorganisms, and helpful information to understand the innate immune system of marine invertebrates.

• Korean Journal of Food Science and Technology
• /
• v.43 no.2
• /
• pp.125-133
• /
• 2011

The purpose of this study was to develop a methodology to detect spinosad which are difficult to analyze by multi-component simultaneous analysis of pesticide residues. We monitored spinosad due to the paucity of related information. The spinosad was determined using HPLC with UV detector at 250 nm. Correlation coefficient ($r^2$) for standard curve of spinosad A and D at standard concentration of 0.1-5.0 mg/kg were 0.999, respectively. Limit of quantitation (LOQ) of HPLC analysis was 0.005 mg/kg while limit of detection (LOD) was 0.001 mg/kg. Recovery experiments were conducted on five representative agricultural products to validate the analytical method. The recovery of proposed methods ranged from 74.9% to 104.0% and relative standard deviations were less than 10%. Spinosad residues were investigated in 16 commodities collected from 22 provinces. In this study, residues on all samples were not detected.