17 research outputs found
Acoustic-based assembly defect detection system
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Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : 곡νμ λ¬Έλνμ μμ©κ³΅νκ³Ό, 2022.2. μ±μ°μ .κΈλ‘λ² μ μ‘°μ
체λ€μ μ νμ νμ§μ ν보νκΈ° μν λ§μ λ
Έλ ₯μλ λΆκ΅¬νκ³ , λ€μν μμΈμ μν μμ° λΆλμ μ§μν΄μ λ°μνλ€. μμ° λΆλ μ νμ΄ μλΉμμκ² μ λ¬λ κ²½μ° μ΄κ²μ μ§μ μ μΈ λΉμ© μ΄μΈμλ λΈλλ μ΄λ―Έμ§λ₯Ό μΌμκ°μ μ€μΆ μμΌ κΈ°μ
κ²½μμ ν격μ μ€ μ μλ μ€μν λ¬Έμ μ΄λ€.
μ΅κ·Ό λ₯λ¬λ κΈ°μ μ λ°μ μΌλ‘ μ μ‘° νμ₯μμ μ΄μ νμ§(anomaly detection)κΈ°λ°μ λ§μ μ°κ΅¬κ° μ΄λ£¨μ΄μ§κ³ μλ€. κ·Έλ¬λ μ΅κ·Ό μ°κ΅¬λ κ΄λ ¨ μ°κ΅¬λ₯Ό μ΄ν΄λ³΄λ©΄ μ£Όλ‘ λΉμ (vision)κ²μ¬λ₯Ό ν΅ν μ νμ νλ©΄ κ²°ν¨μ λ€λ£¨κ±°λ μ§λ λ°μ΄ν°λ₯Ό μ΄μ©ν΄ μμ° μ€λΉμ μν κ²μ¬μ κ΄ν μ°κ΅¬κ° λλΆλΆμ΄λ€. κ·Έλ¬λ μ΄λ¬ν λ°©μμ μ리λ₯Ό λ€λ£¨λ μ€λμ€ μ νμ μ μ©νκΈ°μλ μ ν©νμ§ μλ€. λν ν루μ μλ°±, μμ² κ°λ₯Ό μμ°νλ μ μ‘°μ
μμ μ νμ νμ§κ²μ¬μ μΌμλ₯Ό λΆμ°©νκΈ° μν΄μλ λ§μ μκ°κ³Ό λΉμ©μ΄ μμλλ€.
λ³Έ μ°κ΅¬λ λ¨μΌ μν₯ μΌμλ‘ μΈ‘μ ν μ€νΌμ»€ μΆλ ₯λ°μ΄ν°μ ν©μ± κ³± μ κ²½λ§μ νμ©νμ¬ μμ μ€λμ€ μ νΈμμ μ§μ ννμ νμ΅νλ μ리 λΆλ₯μ λν μ’
λ¨ κ° μ κ·Ό λ°©λ²μ μ μνλ€. 쑰립 κ²°ν¨μ λΆλ₯ μμ
κ³Ό κ΄λ ¨λ λ€μν νν°λ₯Ό νμ΅νκΈ° μν΄ 7κ°μ 컨볼루μ
λ μ΄μ΄κ° μ¬μ©λλ€. λ°μ΄ν°μ
μ μ¬λ¬ λμ μ€νΌμ»€μμ μΆλ ₯λ°μ΄ν°λ₯Ό ν΅ν΄ μμ§λμμΌλ©°, μΌλΆ μ€νΌμ»€ μΆλ ₯μμ νμ΅ν μ§μμ΄ λ€λ₯Έ μ€νΌμ»€ κ²°ν¨ μ¬λΆλ₯Ό νλ¨ ν μ μμμ 보μκ³ , νκ· μ νλ 99\%λ₯Ό λ¬μ±νλ κ²μΌλ‘ λνλ¬λ€.
μ μνλ μ‘°λ¦½κ²°ν¨ κ°μ§ κΈ°λ²μ κΈ°μ‘΄μ 2D ννμ μ
λ ₯μΌλ‘ μ¬μ©νλ λλΆλΆμ λ°©μλ³΄λ€ λμ μ±λ₯μ 보μΈλ€. λν, λ€λ₯Έ μν€ν
μ²μ λΉν΄ μ μ μμ 맀κ°λ³μλ₯Ό κ°μ§κ³ μμ΄, μ€μκ° μ ν νμ§ κ²μ¬μ ν¨μ¨μ μ΄λ€.
λ³Έ μ°κ΅¬λ₯Ό ν΅ν΄ μ μ‘° νμ₯μμ TV, μ°¨λμ© AVNκ³Ό κ°μ΄ μ€νΌμ»€κ° νμ¬λ μ νμ λν 쑰립 곡μ λΆλλ₯ μ κ°μμμΌμ€ κ²μΌλ‘ κΈ°λνλ€.Despite many efforts by global manufacturers to secure product quality, production defects due to various causes continue to occur. If defective products are delivered to consumers, this is an important problem that can damage corporate management by instantly destroying brand image in addition to direct cost.
Recently, with the development of deep learning technology, many studies based on anomaly detection are being conducted in manufacturing sites. However, if you look at the related studies that have been recently studied, most of them deal with the surface defects of products through vision inspection or the state inspection of production facilities using vibration data. However, these methods are not suitable for application to audio products that deal with sound. In addition, it takes a lot of time and money to attach a sensor to apply it to quality inspection of products in a manufacturing industry that produces hundreds or thousands of units per day.
In this paper, we present an end-to-end approach to sound classification that learns representations directly from raw audio signals using speaker output data measured by a single acoustic sensor and a synthetic product neural network. Seven convolutional layers are used to learn various filters related to the classification task of assembly defects. The dataset was collected through output data from multiple speakers, and it was shown that the knowledge learned from the output of some speakers can determine whether other speakers are defective, with 99% accuracy.
The proposed assembly defect detection method shows higher performance than most methods that use the existing 2D representation as input. In addition, it has fewer parameters compared to other architectures, making it efficient for real-time product quality inspection.
Through this study, it is expected that the assembly process defect rate will be reduced for product groups with speakers installed inside the product, such as TVs and AVNs for vehicles.I. μλ‘ 1
1.1 μ°κ΅¬ λ°°κ²½ 1
1.2 μ κ·Ό 4
1.3 μ°κ΅¬λ³΄κ³ μ κ΅¬μ± 5
II. κ΄λ ¨ μ°κ΅¬ 6
2.1 ν©μ±κ³± μ κ²½λ§ 6
2.2 μ μ΄ νμ΅ 10
2.3 λ°μ΄ν° μ¦κ° 11
2.4 μμ κ°μ§(Onset detection) 12
2.4.1 μκ° μμ μμ κ°μ§ 12
2.4.2 μ£Όνμ μμ μμ κ°μ§ 13
2.5 μν₯ μ₯λ©΄ λΆλ₯ 13
III. λ¬Έμ μ μ λ°©λ²λ‘ 14
3.1 쑰립 κ²°ν¨μ μ μ 14
3.2 μ μλ μ’
λ¨κ° μν€ν
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3.2.1 1D CNN ν ν΄λ‘μ§ 19
3.2.2 λ€νΈμν¬ μν€ν
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4.4.2 μ€νΌμ»€ μ§ν₯μ± λΆμ 44
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μ°Έκ³ λ¬Έν 51
Abstract 57μ
2-isopropyl-6-methyl-4-pyrimidinolμ κ΄μ΄λ§€ μ°νλ°μμ© TiO2 μ΄λ§€μ κ°λ°
Thesis (doctoral)--μμΈλνκ΅ λνμ :μμ©ννλΆ,2004.Docto
Acoustic-based assembly defect detection system
κΈλ‘λ² μ μ‘°μ
체λ€μ μ νμ νμ§μ ν보νκΈ° μν λ§μ λ
Έλ ₯μλ λΆκ΅¬νκ³ , λ€μν μμΈμ μν μμ° λΆλμ μ§μν΄μ λ°μνλ€. μμ° λΆλ μ νμ΄ μλΉμμκ² μ λ¬λ κ²½μ° μ΄κ²μ μ§μ μ μΈ λΉμ© μ΄μΈμλ λΈλλ μ΄λ―Έμ§λ₯Ό μΌμκ°μ μ€μΆ μμΌ κΈ°μ
κ²½μμ ν격μ μ€ μ μλ μ€μν λ¬Έμ μ΄λ€.
μ΅κ·Ό λ₯λ¬λ κΈ°μ μ λ°μ μΌλ‘ μ μ‘° νμ₯μμ μ΄μ νμ§(anomaly detection)κΈ°λ°μ λ§μ μ°κ΅¬κ° μ΄λ£¨μ΄μ§κ³ μλ€. κ·Έλ¬λ μ΅κ·Ό μ°κ΅¬λ κ΄λ ¨ μ°κ΅¬λ₯Ό μ΄ν΄λ³΄λ©΄ μ£Όλ‘ λΉμ (vision)κ²μ¬λ₯Ό ν΅ν μ νμ νλ©΄ κ²°ν¨μ λ€λ£¨κ±°λ μ§λ λ°μ΄ν°λ₯Ό μ΄μ©ν΄ μμ° μ€λΉμ μν κ²μ¬μ κ΄ν μ°κ΅¬κ° λλΆλΆμ΄λ€. κ·Έλ¬λ μ΄λ¬ν λ°©μμ μ리λ₯Ό λ€λ£¨λ μ€λμ€ μ νμ μ μ©νκΈ°μλ μ ν©νμ§ μλ€. λν ν루μ μλ°±, μμ² κ°λ₯Ό μμ°νλ μ μ‘°μ
μμ μ νμ νμ§κ²μ¬μ μΌμλ₯Ό λΆμ°©νκΈ° μν΄μλ λ§μ μκ°κ³Ό λΉμ©μ΄ μμλλ€.
λ³Έ μ°κ΅¬λ λ¨μΌ μν₯ μΌμλ‘ μΈ‘μ ν μ€νΌμ»€ μΆλ ₯λ°μ΄ν°μ ν©μ± κ³± μ κ²½λ§μ νμ©νμ¬ μμ μ€λμ€ μ νΈμμ μ§μ ννμ νμ΅νλ μ리 λΆλ₯μ λν μ’
λ¨ κ° μ κ·Ό λ°©λ²μ μ μνλ€. 쑰립 κ²°ν¨μ λΆλ₯ μμ
κ³Ό κ΄λ ¨λ λ€μν νν°λ₯Ό νμ΅νκΈ° μν΄ 7κ°μ 컨볼루μ
λ μ΄μ΄κ° μ¬μ©λλ€. λ°μ΄ν°μ
μ μ¬λ¬ λμ μ€νΌμ»€μμ μΆλ ₯λ°μ΄ν°λ₯Ό ν΅ν΄ μμ§λμμΌλ©°, μΌλΆ μ€νΌμ»€ μΆλ ₯μμ νμ΅ν μ§μμ΄ λ€λ₯Έ μ€νΌμ»€ κ²°ν¨ μ¬λΆλ₯Ό νλ¨ ν μ μμμ 보μκ³ , νκ· μ νλ 99\%λ₯Ό λ¬μ±νλ κ²μΌλ‘ λνλ¬λ€.
μ μνλ μ‘°λ¦½κ²°ν¨ κ°μ§ κΈ°λ²μ κΈ°μ‘΄μ 2D ννμ μ
λ ₯μΌλ‘ μ¬μ©νλ λλΆλΆμ λ°©μλ³΄λ€ λμ μ±λ₯μ 보μΈλ€. λν, λ€λ₯Έ μν€ν
μ²μ λΉν΄ μ μ μμ 맀κ°λ³μλ₯Ό κ°μ§κ³ μμ΄, μ€μκ° μ ν νμ§ κ²μ¬μ ν¨μ¨μ μ΄λ€.
λ³Έ μ°κ΅¬λ₯Ό ν΅ν΄ μ μ‘° νμ₯μμ TV, μ°¨λμ© AVNκ³Ό κ°μ΄ μ€νΌμ»€κ° νμ¬λ μ νμ λν 쑰립 곡μ λΆλλ₯ μ κ°μμμΌμ€ κ²μΌλ‘ κΈ°λνλ€.Despite many efforts by global manufacturers to secure product quality, production defects due to various causes continue to occur. If defective products are delivered to consumers, this is an important problem that can damage corporate management by instantly destroying brand image in addition to direct cost.
Recently, with the development of deep learning technology, many studies based on anomaly detection are being conducted in manufacturing sites. However, if you look at the related studies that have been recently studied, most of them deal with the surface defects of products through vision inspection or the state inspection of production facilities using vibration data. However, these methods are not suitable for application to audio products that deal with sound. In addition, it takes a lot of time and money to attach a sensor to apply it to quality inspection of products in a manufacturing industry that produces hundreds or thousands of units per day.
In this paper, we present an end-to-end approach to sound classification that learns representations directly from raw audio signals using speaker output data measured by a single acoustic sensor and a synthetic product neural network. Seven convolutional layers are used to learn various filters related to the classification task of assembly defects. The dataset was collected through output data from multiple speakers, and it was shown that the knowledge learned from the output of some speakers can determine whether other speakers are defective, with 99% accuracy.
The proposed assembly defect detection method shows higher performance than most methods that use the existing 2D representation as input. In addition, it has fewer parameters compared to other architectures, making it efficient for real-time product quality inspection.
Through this study, it is expected that the assembly process defect rate will be reduced for product groups with speakers installed inside the product, such as TVs and AVNs for vehicles.I. μλ‘ 1
1.1 μ°κ΅¬ λ°°κ²½ 1
1.2 μ κ·Ό 4
1.3 μ°κ΅¬λ³΄κ³ μ κ΅¬μ± 5
II. κ΄λ ¨ μ°κ΅¬ 6
2.1 ν©μ±κ³± μ κ²½λ§ 6
2.2 μ μ΄ νμ΅ 10
2.3 λ°μ΄ν° μ¦κ° 11
2.4 μμ κ°μ§(Onset detection) 12
2.4.1 μκ° μμ μμ κ°μ§ 12
2.4.2 μ£Όνμ μμ μμ κ°μ§ 13
2.5 μν₯ μ₯λ©΄ λΆλ₯ 13
III. λ¬Έμ μ μ λ°©λ²λ‘ 14
3.1 쑰립 κ²°ν¨μ μ μ 14
3.2 μ μλ μ’
λ¨κ° μν€ν
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3.2.1 1D CNN ν ν΄λ‘μ§ 19
3.2.2 λ€νΈμν¬ μν€ν
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3.3 μ‘°λ¦½κ²°ν¨ μ€λμ€ λ°μ΄ν° μ¦κ° 23
IV. μ€ν λ° νκ° 25
4.1 쑰립 κ²°ν¨ λ°μ΄ν°μ
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4.2 μ€ν νκ²½ 29
4.3 νμ΅ λ°©λ² 34
4.4 νκ° 36
4.4.1 λ€νΈμν¬λ³ μ±λ₯ νκ° 38
4.4.2 μ€νΌμ»€ μ§ν₯μ± λΆμ 44
V. κ²°λ‘ 48
μ°Έκ³ λ¬Έν 51
Abstract 57μ
Candidates Campaign Strategies and Vote Shares in the 20th National Assembly Elections
μ κ±°μμ ν보μκ° μ κΆμμκ² μ 곡νλ μ 보λ λ§€μ° μ€μνλ€. 짧μ μ κ±°μ΄λ κΈ°κ° λμ ν보μκ° μ κΆμλ₯Ό λμμΌλ‘ λ€μν ν보 μ λ΅μ νΌμΉλ μ΄μ κ° λ°λ‘ μ¬κΈ°μ μλ€. μ΄λ¬ν μΈμνμ λ³Έ μ°κ΅¬λ ν보μμ ν보 μ λ΅μ νꡬνλ€. κ·Έ μ€μμλ ν보μκ° νμ©νλ ν보 맀체, νΉν μ 거곡보μ μ΄μ μ λλ€. μ°λ¦¬λ μ κ±°κ²°κ³Όμ μν₯μ λ―ΈμΉλ λ³μλ‘ ν보μκ° μ΄λ»κ² μ κ±°λ₯Ό κ·μ νκ³ ν보νλκ°λ₯Ό μ€μνκ² κ³ λ €νλ€. ꡬ체μ μΌλ‘ μ 20λ μ΄μ ν보μμ μ 거곡보λ₯Ό μΈλ¬Ό, μ μ±
, 맀체 μμΈμ μ€μ¬μΌλ‘ μ΄ν΄λ³΄κ³ μ΄λ¬ν νΉμ§μ΄ λνμ¨μ λ―ΈμΉλ μν₯λ ₯μ μΈ‘μ νλ€. μ΄ μ°κ΅¬μ νκ·λΆμ κ²°κ³Όμ λ°λ₯΄λ©΄, ν보μκ° μμ μ μ΄λ¦μ κ°μ‘°ν μλ‘ λνμ¨μ΄ μ¦κ°νλ λ°λ©΄, μ λΉλͺ
μ κ°μ‘°νλ κ²½μ° λνμ¨μ΄ κ°μνλ κ²½ν₯μ 보μΈλ€. λν μμ νλ보λ€λ μ§μꡬ μ΄μ΅κ³Ό κ΄λ ¨ν λ³ΈμΈμ μ
μ μ κ°μ‘°ν λ λνμ¨μ΄ μμΉνλ κ²μΌλ‘ λνλ¬λ€. λ§μ§λ§μΌλ‘, μ κΆμμκ² λ€μν μν΅ μ±λμ μ 곡νλ κ²μ΄ λνμ¨μ κΈμ μ μΈ μν₯μ λ―ΈμΉλ€λ κ²μ νμΈν μ μλ€. μ΄ μ°κ΅¬μ κ²½νμ κ²°κ³Όλ ν보μμ μ κ±° μ λ΅μ΄ μ κ±° κ²°κ³Όμ μν₯μ λ―ΈμΉ μ μμμ 보μ¬μ€λ€.Candidates develop and apply various campaign strategies to win elections. Examining campaign bulletins, this study grasps candidates campaign strategies. Furthermore, we estimate the influences of campaign strategies on vote shares. Campaign bulletin is one of the major campaign tools to communicate with voters. Analyzing campaign bulletins in the 20th National Assembly Elections, we focus on three different factors: candidate, policy, and media. This study tests the influences of each factor on election results. The regression results of this study show that stressing candidates names tends to increase vote shares. However, frequently exposing party names is more likely to affect vote shares negatively. In addition, advertising their activities that have brought economic benefits to their constituents tends to help incumbent candidates collect more votes. Finally, according to the regression results, providing various communication channels for voters can affect vote shares in a positive manner