• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.433-438
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• 1999

This paper describes Active Noise Cancellation/Control(ANC) method that removes the information of the unnecessary noise and doesn't remove the informations of the necessary noise(warning sound, operating sound etc.) for the induced noise of the mechanical system. In this paper, the noise source Is axial fan, and the Feedback Active Noise control method that can effectively control BPF generated from the axial fan is used, and the Filtered-X LMS algorithm for adaptive algorithms is used. The experiments are executed for two case(propagating noise in the duct, emission noise for exterior free field). The part to be removed is BPF noise, and the band-pass filter not to effect to the other frequencies is used. Also, to investigate the effect of the noise reduction for human, we are compared with the results that are controlled for using Loudness before and after. As a results, we are certified that the BPF is decreased only and frequencies outside of BPF are not affected, and we acquire the reduction effects of 6.7 dB Loudness Level, then the frequency to be removed is controlled. Therefore, we can be certified that sound pressure as well as loudness can be effectively decreased for human sound quality

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.247-262
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• 2000

In this paper, the WSANN(Water Stage Analysis with Neural Network) model was presented so as to predict flood water stage at Jindong which has been the major stream gauging station in Nakdong river basin. The WSANN model used the improved backpropagation training algorithm which was complemented by the momentum method, improvement of initial condition and adaptive-learning rate and the data which were used for this study were classified into training and testing data sets. An empirical equation was derived to determine optimal hidden layer node between the hidden layer node and threshold iteration number. And, the calibration of the WSANN model was performed by the four training data sets. As a result of calibration, the WSANN22 and WSANN32 model were selected for the optimal models which would be used for model verification. The model verification was carried out so as to evaluate model fitness with the two-untrained testing data sets. And, flood water stages were reasonably predicted through the results of statistical analysis. As results of this study, further research activities are needed for the construction of a real-time warning of the impending flood and for the control of flood water stage with neural network method in river basin. basin.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.5
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• pp.682-692
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• 2007

This study presents an automatic system to predict parturition time in the crated sows. The system relies on ultrasonic transducers mounted from above along the length of the crate. Using a 40 kHz time of flight (TOF) single envelope wave, the momentary distances between the sensors are measured. Therefore, the local momentary height of the sow and the momentary posture, i.e. standing posture (SDP), kneeling posture (KP), sitting posture (STP) and lateral lying posture (LLP) are determined. Crated sows change their postures from standing to lying and vice versa which follows a characteristic pattern. As parturition approaches, sows exhibit uneasiness, restlessness and the stand up sequence (SUS, the posture transition from LLP to SDP) rate increases because of labor pains. In time series, the SUS rate demonstrates a peak and it happens approximately 0-12 h before parturition. In this paper, the basic parturition threshold value method (BPTVM) and the same hour method (SHM) are proposed for predicting parturition, both of which are based on the SUS rate. The BPTVM mainly detects the peak of the SUS rate. As the SUS rate exceeds the threshold value, the parturition becomes predictable. Moreover, the SHM calculates the difference in the SUS rates between a particular time of day and the corresponding time of the preceding day. Compared to the BPTVM, the SHM can eliminate the circadian rhythm of the SUS rate influenced by feeding behavior. Using the SHM the parturition can be approximately predicted within hours. In an attempt to define the threshold parameters of predicting parturition, a data set with 32 sows of the SUS rate are used to estimate assumable predicting probability. The results show the assumable probability of the parturition prediction within 9 h is 96.9% for the SHM and 84.4% for the BPTVM. Moreover, the SHM can even reach a 75% probability of prediction within three hours of parturition. We conclude that the SHM is more accurate and is more useful for parturition time prediction. When parturition is detected, the proposed algorithm generates a warning signal which can inform human personnel to protect the mother and newborn piglets.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.674-682
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• 2020

Recently, the problem of air pollution has become a global concern due to industrialization and overcrowding. Air pollution can cause various adverse effects on human health, among which respiratory diseases such as asthma, which have been of interest in this study, can be directly affected. Previous studies have used clinical data to identify how air pollutant affect diseases such as asthma based on relatively small samples. This is high likely to result in inconsistent results for each collection samples, and has significant limitations in that research is difficult for anyone other than the medical profession. In this study, the main focus was on predicting the actual asthmatic occurrence, based on data on the atmospheric environment data released by the government and the frequency of asthma outbreaks. First of all, this study verified the significant effects of each air pollutant with a time lag on the outbreak of asthma through the time-lag Pearson Correlation Coefficient. Second, train data built on the basis of verification results are utilized in Deep Learning algorithms, and models optimized for predicting the asthmatic occurrence are designed. The average error rate of the model was about 11.86%, indicating superior performance compared to other machine learning-based algorithms. The proposed model can be used for efficiency in the national insurance system and health budget management, and can also provide efficiency in the deployment and supply of medical personnel in hospitals. And it can also contribute to the promotion of national health through early warning of the risk of outbreak by atmospheric environment for chronic asthma patients.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.6
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• pp.678-686
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• 2019

In this study, marine heat waves on coastal waters of Republic of Korea were detected using satellite-based Sea Surface Temperature Anomaly (SSTA). The detected results were compared with the warm water issues reported by the National Institute of Fisheries Science (NIFS). Marine heat waves detection algorithm using SSTA based on a threshold has proposed. The threshold value was defined as 2℃ for caution and 3℃ for warning issues, respectively. Daily averaged SST data from July to September of 2017-2018 were used to generate SSTA. The satellite-based detection results were classified into nine areas according to the place names used in the NIFS warm water issues. In the comparison of frequency of marine heat waves occurrence to each area with the warm water issue, most areas in the southern coast showed a similar pattern, that is probably NIFS uses spatially well distributed buoys. On the other hand, other sea areas had about two times more satellite detection results. This result seems to be because NIFS only considers the water temperature data measured at limited points. The results of this study are expected to contribute to the development of a satellite-based warm/cold water monitoring system in coastal waters.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.317-321
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• 2022

In response to Incheon tap water accident in 2019, the Ministry of Environment has created the "Comprehensive Measures for Water Safety Management" to improve water operation management, provide systematic technical support, and respond to accidents. Accordingly, K-water is making a smart water supply management system for the entire process of tap water. In order to advance the response to water accidents, it is essential to secure the reliability of real-time water operation data such as flow rate, pressure, and water level, and to develop and apply a warning algorithm in advance using big data analysis techniques. In this paper, various statistical techniques are applied using water supply operation data (flow, pressure, water level, etc) to prepare the foundation for the selection of the optimal operating range and advancement of the monitoring and alarm system. In addition, the arrival time is analyzed through cross-correlation analysis of changes in raw water turbidity between the water intake and water treatment plants. The purpose of this paper is to study the model that predicts the raw water turbidity of a water treatment plant by applying raw water turbidity data considering the time delay according to the flow rate change.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.1075-1084
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• 2024

Efforts to expand the installation of devices that assist visually impaired individuals in their mobility are ongoing, but there are significantly fewer devices installed indoors compared to outdoors, causing considerable inconvenience for indoor navigation. Therefore, this paper aims to address these issues by applying the results of machine learning using YOLO(You Only Look Once) to a Raspberry Pi and by researching techniques to reduce errors through the trilateration method of UWB(Ultra-Wideband) sensors, applying it with a Kalman filter. The research results implemented an object recognition algorithm with a comprehensive accuracy of 91.7% using YOLO technology. Based on this object recognition, the direction (left, right, or front) was determined using the distance difference between two ultrasonic sensors set at an angle difference of 15 degrees. A distance of up to 1.5m was accepted through an infrared sensor to output a warning message according to the distance. The distance between the user's tag and the fixed three anchors was measured indoors through a UWB sensor, and the user's location was also measured indoors by linking the distance value with the three-side positioning technique.

• Journal of Soil and Groundwater Environment
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• v.7 no.4
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• pp.31-39
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• 2002

Pollutants from industry, mining, agriculture, and other sources have contaminated sediments in many surface water bodies. Sediment contamination poses a severe threat to human health and environment because many toxic contaminants that are barely detectable in the water column can accumulate in sediments at much higher levels. The purpose of this study was to make optimal treatment and disposal plan o( sediment for water quality improvement in small-scale resevoir based on an evaluation of degree of contamination. The degree of contamination were investigated for 23 samples of 9 site at different depth of sediment in small-scale J river. Results for analysis of contaminated sediments were observed that copper concentration of 4 samples were higher than the regulation of hazardous waste (3 mg/L) and that of all samples were exceeded soil pollution warning levels for agricultural areas. Lead and mercury concentration of all samples were detected below both regulations. Necessary of sediment dredge was evaluated for organic matter and nutrient through standard levels of Paldang lake and the lower Han river in Korea and Tokyo bay and Yokohama bay in Japan. The degree of contamination for organic matter and nutrient was not serious. Compared standard levels of Japan, America, and Canada for heavy metal, contaminated sediment was concluded as lowest effect level or limit of tolerance level because standard levels of America and Canada was established worst effect of benthic organisms. The optimal treatment method of sediment contained heavy metal was cement-based solidification/stabilization to prevent heavy metal leaching.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.