• Journal of Information Processing Systems
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• 제19권2호
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• 대한원격탐사학회지
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• 제13권1호
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• pp.47-56
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• 1997

본 연구소에서는 호소 수질오염의 중요한 관심대상인 영양상태를 평가하기 위해 원격탐 사기법을 적용하였다. 원격탐사기법을 적용하는데 있어서 기존의 회귀식을 이용한 방법과는 달리 분류기법을 사용하여 영양상태를 평가하였다. 부영양화는 조류의 이상증식에 의해 유발되므로, 수 체의 조류농도와 밀접한 항목인 클로로필 a와 투명도를 원격탐사 데이터에 적용하였다. 본 연구 에서 영향상태의 분류는 최대우도법과 최소거리법을 이용하였으며, 다음과 같은 결과를 얻었다. 첫째, 광역수계의 영양상태 평가시 원격탐사 데이터를 적용함에 있어 기초적인 분류기법만을 수 행하여도 70%이상의 정확도를 얻을 수 있었다. 둘째, 분류정확도면에서 최소거리법이 최대우도법에 비하여 양호하게 나타났다. 이것은 샘플이 정규분포를 이루고는 있으나 통계적인 기법을 적용하기에는 샘플수가 너무 적은 것에 기인한 것 으로 차후 통계적 분포에 영향을 받지 않는 인공신경망을 이용한 분류기법의 도입이 요구된다. 셋째, 본 연구결과를 이용하면 수계의 영양상태를 신속하고 주기적이며 가시적인 분석평가를 할 수 있어 호소의 영양상태 진행정도에 따라 적절한 대응책을 수립하는데 기초자료로서 활용할 수 있을 것으로 기대된다.

• Journal of Astronomy and Space Sciences
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• 제41권3호
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• pp.149-158
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• 2024

This study developed a machine learning-based methodology to classify gravitational wave (GW) signals from black hol-eneutron star (BH-NS) mergers by combining convolutional neural network (CNN) with conditional information for feature extraction. The model was trained and validated on a dataset of simulated GW signals injected to Gaussian noise to mimic real world signals. We considered all three types of merger: binary black hole (BBH), binary neutron star (BNS) and neutron starblack hole (NSBH). We achieved up to 96% correct classification of GW signals sources. Incorporating our novel conditional information approach improved classification accuracy by 10% compared to standard time series training. Additionally, to show the effectiveness of our method, we tested the model with real GW data from the Gravitational Wave Transient Catalog (GWTC-3) and successfully classified ~90% of signals. These results are an important step towards low-latency real-time GW detection.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2008년도 춘계 학술발표회 초청강연 및 논문집
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• pp.1466-1483
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• 2008

This paper discusses the development of an up-to-date computerized CPT (Cone Penetration Test) based soil engineering classification system to provide geotechnical engineers with a handy tool for their daily design activities. Five CPT soil engineering classification systems are incorporated in this effort. They include the probabilistic region estimation and fuzzy classification methods, both developed by Zhang and Tumay, the Schmertmann, the Douglas and Olsen, and the Robertson et al. methods. In the probabilistic region estimation method, a conformal transformation is used to determine the soil classification index, U, from CPT cone tip resistance and friction ratio. A statistical correlation is established between U and the compositional soil type given by the Unified Soil Classification System (USCS). The soil classification index, U, provides a soil profile over depth with the probability of belonging to different soil types, which more realistically and continuously reflects the in-situ soil characterization, which includes the spatial variation of soil types. The CPT fuzzy classification on the other hand emphasizes the certainty of soil behavior. The advantage of combining these two classification methods is realized through implementing them into visual basic software with three other CPT soil classification methods for friendly use by geotechnical engineers. Three sites in Louisiana were selected for this study. For each site, CPT tests and the corresponding soil boring results were correlated. The soil classification results obtained using the probabilistic region estimation and fuzzy classification methods are cross-correlated with conventional soil classification from borings logs and three other established CPT soil classification methods.

• Asian Pacific Journal of Cancer Prevention
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• 제15권9호
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• pp.4109-4115
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• 2014

Background: Accurate assessment of disease progression requires proper understanding of natural disease process which is often hidden and unobservable. For this purpose, disease status should be clearly detected. But in most diseases it is not possible to detect such status. This study, therefore, aims to present a model which both investigates the unobservable disease process and considers the error probability in diagnosis of disease states. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at the Iran Cancer Institute from 1995 to 1999 were analyzed. Moreover, to estimate and assess the effect of demographic, diagnostic and clinical factors as well as medical and post-surgical variables on transition rates and the probability of misdiagnosis of relapse, a hidden Markov multi-state model was employed. Results: Classification errors of patients in alive state without a relapse ($e_{21}$) and with a relapse ($e_{12}$) were 0.22 (95% CI: 0.04-0.63) and 0.02 (95% CI: 0.00-0.09), respectively. Only variables of age and number of renewed treatments affected misdiagnosis of relapse. In addition, patient age and distant metastasis were among factors affecting the occurrence of relapse (state1${\rightarrow}$state2) while the number of renewed treatments and the type and extent of surgery had a significant effect on death hazard without relapse (state2${\rightarrow}$state3)and death hazard with relapse (state2${\rightarrow}$state3). Conclusions: A hidden Markov multi-state model provides the possibility of estimating classification error between different states of disease. Moreover, based on this model, factors affecting the probability of this error can be identified and researchers can be helped with understanding the mechanisms of classification error.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.283-284
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• 2019

In the last decades patient's suffering with Parkinson's disease is increasing at a rapid rate and as per prediction it will grow more rapidly as old age population is increasing at a rapid rate through out the world. As the performance of wearable sensor based approach reached to a new height as well as powerful machine learning technique provides more accurate result these combination has been widely used for assessment of various neurological diseases. ON state is the state where the effect of medicine is present and OFF state the effect of medicine is reduced or not present at all. Classification of ON/OFF state for the Parkinson's disease is important because the patients could injure them self due to freezing of gait and gait related problems in the OFF state. in this paper wearable sensor based approach has been used to collect the data in ON and OFF state and machine learning techniques are used to automate the classification based on the gait pattern. Supervised machine learning techniques able to provide 97.6% accuracy while classifying the ON/OFF state.

• 한국물환경학회지
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• 제35권3호
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• pp.248-256
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• 2019

Most of the lakes in Korea are artificial, and their limnological characteristics are significantly different from those of natural lakes in other countries. In this study, the relationship between trophic state parameters was investigated, based on summer average data of the upper layer, in 81 lakes in Korea, 2013-2017. Compared with trends of foreign natural lakes, chlorophyll a (Chl.a) concentration was slightly lower at the same total phosphorus (TP) concentration, and transparency (Secchi depth, SD) was noticeably lower at the same Chl.a concentration. This is because of excessive allochthonous loading of non-algal material during the monsoon period, and the reduction in phosphorus availability to algal growth, by light limitation and short hydraulic residence time. Considering these characteristics, we suggested site-specific thresholds of trophic state classification for Chl.a, TP and SD, based on annual average data at the upper layer of lakes ($3-10{\mu}g\;L^{-1}$ of Chl.a measured by UNESCO method; $13-33{\mu}g\;L^{-1}$ of TP; 1.6-3.2 m of SD for mesotrophic state class, respectively). The threshold value of TP for each trophic state class, corresponded to the upper value of previously reported range, and that of SD was out of the range. We suggested applying only TP and Chl.a in assessment of trophic state of lakes in Korea, excluding SD.

• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2000년도 International Symposium:on the Maritime Management Systems for Safer and Cleaner Seas in the New Millennium
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• pp.102-160
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• 2000

• 한국해양공학회지
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• 제14권3호
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• pp.100-105
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• 2000

Port State Control (PSC) is the inspection of foreign ships in national ports for the purpose of verifying that the condition of the ships and its equipments comply with the requirement of international conventions and the ship is manned and operated in compliance with applicable international laws. On the other hand, check items in PSC are nearly similar to periodical survey of Classification Societies, because they have the same background regarding safety and maritime pollution prevention. The purpose of this study is to develope computer-aided information system for ship inspection item which is useful for effective implementation of Port State Control. For this work, the status of PSC is reviewed, and the related scheme of ship survey system in Classification Societies is investigated. On these bases, a computer software integrated database system and object-oriented technique is developed. The developed system is expected helpful to establish and maintain an effective system of Port State Control.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2000년도 추계학술대회 논문집
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• pp.165-170
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• 2000

Port State Control (PSC) is the inspection of foreign ships in national ports for the purpose of verifying th\ulcorner the condition of the ships and its equipments comply with the requirement of international conventions and the ship is manned and operated in compliance with applicable international laws. On the other hand, check items in PSC are nearly similar to periodical survey of Classification Societies, because they have the same background regarding safety and maritime pollution prevention. The purpose of this study is to develope computer-aided information systems for ship inspection item which is useful for effective implementation of Port State Control. For this work, the status of PSC is reviewed, and the related scheme of ship survey system in Classification Societies is investigated. On these bases, a computer software integrated database system and object-oriented technique is developed. The developed system is expected helpful to establish and maintain an effective system of Port State Control.