• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.10-18
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• 2020

Among various contents industry, this study especially focused on music industry and tried to develop a prediction model for music box office using deep learning. The deep learning prediction model designed to predict music chart-in period based on 17 variables -singer power, singer influence, featuring singer power, featuring singer influence, number of participating singers, gender of participating singers, lyric writer power, composer power, arranger power, production agency power, distributing agency power, title track, LIKEs on streaming platform, comments on streaming platform, pre-promotion article, teaser-video view, first-week performance. Additionally we conducted a linear regression analysis to sort out factors, and tried to compare the prediction performance between the original DNN prediction model and the DNN model made of sorted out factors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1898-1908
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• 2017

Accurately Diagnosing and managing disease in livestock can help sustainable livestock productivity and maintain human health. Maintaining the health of livestock is an important part of human health. The prediction of calf diseases is carried out by pre-processing the calf biometric data. calf information is used as information for calf birth history, calf biometric information, environmental information on housing, and disease management. It can be developed as an ontology and used as a knowledge base. The Bayesian network was used and inferred in the process of analyzing the correlations of calf diseases. Prediction of diseases based on knowledge of calf disease on calf diseases name, causes, occur timing, care and symptoms, etc., will be able to respond to accurate disease treatment and prevent other livestock from being infected in advance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.142-148
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• 2015

Due to advancements in technology and manufacturing capability, it is not uncommon that life tests yield no or few failures at low stress levels. In these situations it is difficult to analyse lifetime data and make meaningful inferences about product or system reliability. For some products or systems whose performance characteristics degrade over time, a failure is said to have occurred when a performance characteristic crosses a critical threshold. The measurements of the degradation characteristic contain much useful and credible information about product or system reliability. Degradation measurements of the performance characteristics of an unfailed unit at different times can directly relate reliability measures to physical characteristics. Reliability prediction based on physical performance measures can be an efficient and alternative method to estimate for some highly reliable parts or systems. If the degradation process and the distance between the last measurement and a specified threshold can be established, the remaining useful life is predicted in advance. In turn, this prediction leads to just in time maintenance decision to protect systems. In this paper, we describe techniques for mapping product or system which has degrading performance parameter to the associated classical reliability measures in the performance domain. This paper described a general modeling and analysis procedure for reliability prediction based on one dominant degradation performance characteristic considering pseudo degradation performance life trend model. This pseudo degradation trend model is based on probability modeling of a failure mechanism degradation trend and comparison of a projected distribution to pre-defined critical soft failure point in time or cycle.

• Journal of Ocean Engineering and Technology
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• v.35 no.1
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• pp.91-97
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• 2021

Marine accidents caused by ships have brought about economic and social losses as well as human casualties. Most of these accidents are caused by small and medium-sized ships and are due to their poor conditions and insufficient equipment compared with larger vessels. Measures are quickly needed to improve the conditions. This paper discusses a video-integrated collision prediction and fall detection system to support the safe navigation of small- and medium-sized ships. The system predicts the collision of ships and detects falls by crew members using the CCTV, displays the analyzed integrated information using automatic identification system (AIS) messages, and provides alerts for the risks identified. The design consists of an object recognition algorithm, interface module, integrated display module, collision prediction and fall detection module, and an alarm management module. For the basic research, we implemented a deep learning algorithm to recognize the ship and crew from images, and an interface module to manage messages from AIS. To verify the implemented algorithm, we conducted tests using 120 images. Object recognition performance is calculated as mAP by comparing the pre-defined object with the object recognized through the algorithms. As results, the object recognition performance of the ship and the crew were approximately 50.44 mAP and 46.76 mAP each. The interface module showed that messages from the installed AIS were accurately converted according to the international standard. Therefore, we implemented an object recognition algorithm and interface module in the designed collision prediction and fall detection system and validated their usability with testing.

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.31-39
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• 2021

In Korea, more than half of work-related fatalities have occurred on construction sites. To reduce such occupational accidents, safety inspection by government agencies is essential in construction sites that present a high risk of serious accidents. To address this issue, this study developed risk prediction models of serious accidents in construction sites using five machine learning methods: support vector machine, random forest, XGBoost, LightGBM, and AutoML. To this end, 15 proactive information (e.g., number of stories and period of construction) that are usually available prior to construction were considered and two over-sampling techniques (SMOTE and ADASYN) were used to address the problem of class-imbalanced data. The results showed that all machine learning methods achieved 0.876~0.941 in the F1-score with the adoption of over-sampling techniques. LightGBM with ADASYN yielded the best prediction performance in both the F1-score (0.941) and the area under the ROC curve (0.941). The prediction models revealed four major features: number of stories, period of construction, excavation depth, and height. The prediction models developed in this study can be useful both for government agencies in prioritizing construction sites for safety inspection and for construction companies in establishing pre-construction preventive measures.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.123-138
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• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.554-561
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• 2008

Prediction technique of user's intention can be used as a post-processing method for reducing the search space of an automatic speech recognizer. Prediction technique of system's intention can be used as a pre-processing method for generating a flexible sentence. To satisfy these practical needs, we propose a statistical model to predict speakers' intentions that are generalized into pairs of a speech act and a concept sequence. Contrary to the previous model using simple n-gram statistic of speech acts, the proposed model represents a dialogue history of a current utterance to a feature set with various linguistic levels (i.e. n-grams of speech act and a concept sequence pairs, clue words, and state information of a domain frame). Then, the proposed model predicts the intention of the next utterance by using the feature set as inputs of CRFs (Conditional Random Fields). In the experiment in a schedule management domain, The proposed model showed the precision of 76.25% on prediction of user's speech act and the precision of 64.21% on prediction of user's concept sequence. The proposed model also showed the precision of 88.11% on prediction of system's speech act and the Precision of 87.19% on prediction of system's concept sequence. In addition, the proposed model showed 29.32% higher average precision than the previous model.

• Journal of Astronomy and Space Sciences
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• v.13 no.1
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• pp.32-39
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• 1996

Voyager IRIS (Infrared Interferometer Spectrometer) observations of Jupiter's Great Red Spot (GRS) have been examined in order to extract the vertical distribution of phosphine. To the accuracy than can be achieved from this approach, there appears to be no difference between the PH3 distribution over the GRS compared with the distribution over the neighboring South Tropical Zone. This result is at variance with a pre-Voyager prediction of an enhancement of PH3 over the GRS resulting in the preferential production of red phosphorous in this location on the planet (Prinn & Lewis 1975). The composition of the red material remains an open question.

• The Korea Genome Conference
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• 2002.08a
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• pp.46.1-46.1
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• 2002