• Title/Summary/Keyword: classification of real numbers

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Real-time Laying Hens Sound Analysis System using MFCC Feature Vectors

  • Jeon, Heung Seok;Na, Deayoung
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.3
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    • pp.127-135
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    • 2021
  • Raising large numbers of animals in very narrow environments such as laying hens house can be very damaged from small environmental change. Previously researched about laying hens sound analysis system has a problem for applying to the laying hens house because considering only the limited situation of laying hens house. In this paper, to solve the problem, we propose a new laying hens sound analysis model using MFCC feature vector. This model can detect 7 situations that occur in actual laying hens house through 9 kinds of laying hens sound analysis. As a result of the performance evaluation of the proposed laying hens sound analysis model, the average AUC was 0.93, which is about 43% higher than that of the frequency feature analysis method.

Application of Symbolic Representation Method for Fault Detection and Clustering in Semiconductor Fabrication Processes (반도체공정 이상탐지 및 클러스터링을 위한 심볼릭 표현법의 적용)

  • Loh, Woong-Kee;Hong, Sang-Jeen
    • Journal of KIISE:Computing Practices and Letters
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    • v.15 no.11
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    • pp.806-818
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    • 2009
  • Since the invention of the integrated circuit (IC) in 1950s, semiconductor technology has undergone dramatic development up to these days. A complete semiconductor is manufactured through a diversity of processes. For better semiconductor productivity, fault detection and classification (FDC) has been rigorously studied for finding faults even before the processes are completed. For FDC, various kinds of sensors are attached in many semiconductor manufacturing devices, and sensor values are collected in a periodic manner. The collection of sensor values consists of sequences of real numbers, and hence is regarded as a kind of time-series data. In this paper, we propose an algorithm for detecting and clustering faults in semiconductor processes. The proposed algorithm is a modification of the existing anomaly detection algorithm dealing with symbolically-represented time-series. The contributions of this paper are: (1) showing that a modification of the existing anomaly detection algorithm dealing with general time-series could be used for semiconductor process data and (2) presenting experimental results for improving correctness of fault detection and clustering. As a result of our experiment, the proposed algorithm caused neither false positive nor false negative.

A Clinical Study of Tinnitus (耳鳴에 관한 임상적 연구)

  • Choi, In-Hwa
    • The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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    • v.14 no.2
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    • pp.134-145
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    • 2001
  • Introduction: Noises in the ear, whether real or imagined, are called tinnitus. Subjective causes of tinnitus(which is heard only by the patient) are extremely common and the majority of them are treated conservatively. For certain individuals their tinnitus is a major handicap; for others a trivial concern. The most common from of subjective tinnitus is a rushing, hissing or buzzing noise; it is frequently associated with sensorineural heanng loss. The patient may be unaware of the hearing loss, especially if it is a high frequency deficit of moderate severity. The character of the tinnitus may give a clue to the etiology. But the patient often has difficulty in explaining his/her tinnitus in absolute terms, as they have no other tinnitus with which to compare it but their own Tinnitus, like pain, is a subjective state and trying to objectively assess the severity is problematic. Audiological techniques to match subjective loudness to machine-produced noise may offer some help, in that sound intensity matches can bear little correspondence to subjective complaint. In spite of many studies, most patients presently seen complaining of tinnitus are told by their doctors that there is no treatment and that they will have to learn to live with this symptom. Objectives: To perform a clinical analysis of tinnitus and estimate the efficacy of Oriental Medical treatment according to the Byeonjeung(辨證). Subject: We studied 34 patients with complaints of tinnitus who had visited Pundang Cha Oriental Medicine Hospital Department of Otorhinolaryngology from March 1998 to February 2000. All of them had been treated 2 or 3 times a week with acupuncture treatment and had taken herbs according to the Byeonjeung(辨證) method. It was therefore possible for me to know whether their symptoms improved or not. Parameters Observed and Method: We treated them with acupuncture & herb-medication. Sometimes we gave them moxibustion or negative therapy with bloodletting at the acupuncture points(耳門, 聽宮, 聽會). Parameters Observed 1) Distribution of age & sex 2) Chief complaints 3) The sites of tinnitus 4) The quality of tinnitu 5) The duration of disease 6) The problem induced tinnitus 7) Factors increasing disease severity 8) The classification of the Byeonjeung(辨證) 9) The efficacy of treatments Results: 1. Age and sex distribution: The most common occurrence was found in males in their twenties: 6 males($17.7\%$), and in females in their thirties and over sixty: 8 females($23.5\%$). Total patient numbers for men and women were 20 men($58.8\%$), 14 women ($41.2\%$). 2. The most frequent major complaints were hearing disturbances related to tinnitus; and dizziness with tinnitus; each comprising 10 cases($29.4\%$). There were also 7 patients($20.6\%$) with only tinnitus. 3. Tinnitus sites: 13($38.2\%$) said that they felt tinnitus in both ears, equally. In the right ear, 9($26.5\%$), in the left, 6($17.7\%$). 4. The most frequent descriptive symptoms of tinnitus were: humming, hissing, buzzing etc. 5. The duration of disease. 14cases($41.2\%$) had a duration of less than 1 year. 6. 15cases($44.1\%$) complained that it was hard to watch TV or make a phone call because of tinnitus. 10 cases($29.4\%$) complained about depression. 7. Factors increasing severity of tinnitus: ⅰ) fatigue: 18cases($52.9\%$) ⅱ) stress/ tension: 10 cases($29.4\%$) ⅲ) alcohol and tobacco: 5cases($l4.7\%$) 8. Classification through Byeonjeung : ⅰ) 19 cases($55.9\%$) were classified as showing Deficiency syndrome. ⅱ) 15 cases($44.l\%$) were classified as showing Excess syndrome. The deficiency of Qi was 7($20.6\%$), deficiency of Xue, 8($23.5\%$) and insufficiency of the Kidney Yin & Yang, 4($11.8\%$). The flare of Liver fire was 8($23.5\%$) and phlegm-fire, 7($20.6\%$), 9. The efficacy of treatments showed: an improvement in 17cases($50.0\%$); no real improvement or changes in 13 cases($38.2\%$); and some worsening in 4 cases($11.8\%$). In the group with deficiency in Qi, 4($57.1\%$) improved, 1($14.3\%$) showed no change and 2($28.6\%$) were aggravated. In the cases of deficiency in Xue, 6($75.0\%$) improved, 2($25.0\%$) showed no change. In the cases of insufficiency of Kidney Yin & Yang, 3($75.0\%$) showed no change and 1($25.0\%$) were aggravated. In the group of flare of Liver fire, 4($50.0\%$) improved, 3($37.5\%$) no change and 1($12.5\%$) were aggravated. In the cases of phlegm-fire, 3($42.9\%$) improved, 4($57.1\%$) showed no change. Conclusion: We would recommend that any further studies of tinnitus utilize trial treatments of longer than 2 months duration, as any positive effects observed in our study showed that improvement occurred fairly slowly. And we suggest that this study could be utilized as a reference for clinical Oriental Medical treatment of tinnitus. If we try to apply music or sound therapy treatment properly combined with ours, we expect it to provide psycological stability in addition to inducing masking effects, even though it may not directly decrease or completely remove tinnitus.

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Detection of Phantom Transaction using Data Mining: The Case of Agricultural Product Wholesale Market (데이터마이닝을 이용한 허위거래 예측 모형: 농산물 도매시장 사례)

  • Lee, Seon Ah;Chang, Namsik
    • Journal of Intelligence and Information Systems
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    • v.21 no.1
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    • pp.161-177
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    • 2015
  • With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.