61 research outputs found
κ²°μ κ²½κ³μ μ΄ν μ μΊ‘μ λ€νΈμν¬λ₯Ό μ΄μ©ν μ μ¬κ³΅κ° νΉμ±λ°±ν° λΆμ
νμλ
Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : μμ°κ³Όνλν νλκ³Όμ κ³μ°κ³Όνμ 곡, 2022.2. κ°λͺ
μ£Ό.While the deep learning model produces overwhelming performance in many domains, it is not known what latent space the deep learning model embedding, what features it learns, and how it separates features.
An accurate understanding of the learning process of deep learning is not perfect until now and is still an open problem.
In this thesis, we try to broaden our understanding of the latent space of the deep neural network in two ways.
In the first chapter, we experimentally investigate the relationships with the vision boundary in the latent space of the deep neural network through several toy experiments.
The decision boundary is obtained by using and adversarial attack methods in the latent space where deep neural network embeds.
We analyze the relationship between the decision boundary and the latent space manual obtained by perturbing the image.
In the second chapter, the characteristics of the latent space is examined by constraining a network architecture design.
We propose a new network module called an attention-style capsulenet with improved version of capsulenet.
The value of each capsule is perturbed to determine which image feature is trained by the deep neural network.λ₯λ¬λ λͺ¨λΈμ΄ λ§μ λλ©μΈμμ μλμ μΈ μ±λ₯μ λ΄λλ° λ°ν΄ λ₯λ¬λ λͺ¨λΈμ΄ μ΄λ€ μ μ¬ κ³΅κ°μ λ§λλμ§, μ΄λ€ νΉμ§λ₯Ό νμ΅ νλμ§ μ νν μλ €μ§μ§ μκ³ μλ€.
λ₯λ¬λμ νμ΅ κ³Όμ μ λν μ νν μ΄ν΄λ νμ¬κΉμ§ μλ²½νμ§ μκ³ μ΄λ¦° λ¬Έμ μ΄λ€.
μ΄λ² μ°κ΅¬μμλ ν¬κ² 2κ°μ§ λ°©λ²μΌλ‘ λ₯λ¬λμ΄ νμ΅ν μ μ¬κ³΅κ°μ λν΄ μ΄ν΄λ₯Ό λνκ³ μ νμλ€.
첫λ²μ§Έ μ±ν°μμλ λ₯λ¬λ λͺ¨λΈμ μ μ¬κ³΅κ°μμ κ²°μ κ²½κ³λ₯Ό μ΄μ©νμ¬ μ μ¬κ³΅κ°μ νΉμ±μ μ¬λ¬ μ€νλ€μ ν΅ν΄ λΆμνμλ€.
μ λμ 곡격 λ°©λ²μ μ΄μ©ν΄μ κ²°μ κ²½κ³ λ²‘ν°λ₯Ό ꡬνκ³ μΈνμ λ
Έμ΄μ¦λ₯Ό μΆκ°νμ¬ μ μ¬κ³΅κ°μ λ€μ체 벑ν°λ₯Ό ꡬνμλ€.
κ²°μ κ²½κ³ λ²‘ν°μ μ μ¬κ³΅κ°μ λ€μ체 λ²‘ν° μ¬μ΄μ κ΄κ³λ₯Ό ν΅ν΄ μ μ¬κ³΅κ°μ λ€μ체μ ꡬμ±μ λΆμνμλ€.
λλ²μ§Έ μ±ν°μμλ μΊ‘μμ΄λΌλ νΉμν μ€κ³λ‘μ μ μ¬κ³΅κ°μ μ ννμ¬ νμ΅λ μ μ¬κ³΅κ°μ νΉμ§μ μ΄ν΄λ³΄μλ€.
λ€νΈμν¬ κ΅¬μ‘°λ μΊ‘μλ·μ κ°μ ν μ΄ν
μ
μ€νμΌμ μΊ‘μλ·μ μ μνκ³ , κ° μΊ‘μλ€μ κ°μ λ³λμμΌ λ₯λ¬λ λͺ¨λΈμ΄ μ€μ μ΄λ―Έμ§κ³΅κ°μ μ΄λ€ νΉμ§μ λΆμνμ¬ μΊ‘μ μ μ¬κ³΅κ°μΌλ‘ 맀ννλμ§ νμΈνκ³ μΈν λ°μ΄ν°μμμ λ³νμ λν μΊ‘μκ°μ λ³λμ λΆμνμλ€.1 Introduction 1
2 Relation between Feature Manifold and Decision Boundary 4
2.1 Related Work 7
2.1.1 Manifold Hypothesis 7
2.1.2 Manifold Learning Methods 8
2.1.3 Adversarial Attack 12
2.1.4 Explain AI (Visualization methods) 14
2.2 Distribution of angles between latent manifold and the decision boundary 16
2.2.1 Experiment detail 16
2.2.2 Experiment results 18
2.3 Near-local manifold curvature 23
2.3.1 Experiment detail 23
2.3.2 Experiment results 23
2.4 Miscellaneous experiments 28
2.4.1 Does adversarial attack really mean a vulnerability in deep learning models 28
2.4.2 Is the manifold's shape related to the performance of the model 30
3 Attention style Capsulenet 32
3.1 Related Works 36
3.2 Proposed Method 39
3.2.1 Primary Caps Layer 40
3.2.2 Capsule Activation 40
3.2.3 Conv Caps Layer 41
3.2.4 Fully Conv Caps Layer 44
3.2.5 Margin Loss and Reconstruction Regularizer 44
3.3 Experiments 46
3.3.1 Classiffication Results on MNIST and affNIST 47
3.3.2 Classiffication Results on CIFAR-10 49
3.3.3 Robustness to hyperparameters 51
3.3.4 Transformation Equivariance 52
4 Conclusion and Future Works 57λ°
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λκΉμ§μ μ μ°©κ³Όμ μ λλ©΄μμ§μ κ·Όκ±°ν΄μ 곡κ°μ μΌλ‘ λΆμνκ³ , λ³νμ μμΈκ³Ό νΉμ±μ νμ
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μΌλ‘ μΈμλμ΄μ¨ νκ΅ μ£Όν νμ₯μ€μ λ³νκ³Όμ μ 그리 λ¨μνμ§ μμλ€. μΌλ³Έμμ νκ΅μ κ·Έλ¦¬κ³ μμμμ΄ κ²Ήμ³μ§κ³ , μ£Όνμ λ³νκ³Όμ μ λΆμνλ©΄μ, κ°λ₯ν κΈ°μ μ λ²μ μμμ, νκ΅μΈμ μνλ¬Ένλ₯Ό λ΄μμ¨ κ³Όμ μ΄ μμλ€. μμΌλ‘λ μ£Όννμ₯μ€μ 기본ꡬμ±, λΆκ°κ΅¬μ±, μ€λΉ, μ¬λ£ λ±μ΄ κ°μ λλ©΄μ λ³νλ₯Ό κ²ͺμ κ²μ΄λ€. κ·Έ λ³νμ λ°νμ νκ΅ μ£Όννμ₯μ€μ μμ¬μ νΉμ±μ λν μ΄ν΄κ° νμνλ€λ μ μμ μ΄ μ°κ΅¬μ μμκ° μλ€.N
A study on the direct dehydrogenation of n-butane over Pt/Sn/Al2O3catalyst: Effect of promoter
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : ννμ물곡νλΆ, 2014. 8. μ‘μΈκ·.λ³Έ μ°κ΅¬μμλ λ
Έλ₯΄λ§-λΆνμ μ§μ νμμν 곡μ μ ν΅ν΄ λ
Έλ₯΄λ§-λΆν
λ° 1,3-λΆνλμμ μ μ‘°νκΈ° μν μ΄λ§€ μ°κ΅¬λ₯Ό μννμλ€. λ¨Όμ κΈ°λ³Έ μ΄λ§€λ‘ Pt/Sn/Al2O3 μ΄λ§€λ₯Ό μ μ νμκ³ , λΉνμ±νλ₯Ό μ΅μ νκ³ νμ±μ μ¦κ°μν€κΈ° μν΄ λ€μν μ‘°μ΄λ§€λ₯Ό νμνμλ€. λ¨Όμ , λ΄μ²΄μ μ°μ μ μ‘°μ νκΈ° μνμ¬ μ칼리 κΈμμ μ‘°μ΄λ§€λ‘ λμ
ν Pt/Sn/M/Al2O3 (M=Li, Na, K, Rb) μ΄λ§€λ₯Ό μμ°¨μ ν¨μΉ¨λ²μΌλ‘ μ μ‘°νμμΌλ©°, μκΈ° μ΄λ§€μ μ칼리 κΈμμ λμ
μ΄ μ΄λ§€μ λ°μ νμ±μ λ―ΈμΉλ μν₯μ μ‘°μ¬νμλ€. XRD, ICP-AES λ° XPS λΆμμ ν΅νμ¬ μ΄λ§€κ° μ±κ³΅μ μΌλ‘ μ μ‘°λ κ²μ νμΈνμμΌλ©°, μ΄λ§€μ μ°νΉμ±μ΄ λ°μ νμ±μ λ―ΈμΉλ μν₯μ μ΄ν΄λ³΄κΈ° μνμ¬ μλͺ¨λμ μΉμ¨νμ°©μ€νμ μννμλ€. κ·Έ κ²°κ³Ό, Pt/Sn/M/Al2O3 (M=Li, Na, K, Rb) μ΄λ§€μ μ°λμ΄ κ°μν¨μ λ°λΌ μ½ν¬ νμ±μ΄ κ°μνκ³ λΆν
λ° 1,3-λΆνλμμ μμ±λμ΄ μ¦κ°νλ κ²½ν₯μ 보μλ€. λν, νμ±κΈμμ νμ±μ¦μ§μ μνμ¬ μ칼리 κΈμμΈμ λ€μν μ μ΄ κΈμμ μ‘°μ΄λ§€λ‘ λμ
ν Pt/Sn/M/Al2O3 (M=Zn, In, Y, Bi, Ga) μ΄λ§€λ₯Ό μμ°¨μ ν¨μΉ¨λ²μ μ΄μ©νμ¬ μ μ‘°νμκ³ , μ§μ νμμν λ°μμ μ μ©νμλ€. μκΈ° μ΄λ§€λ XRD, ICP-AES λ° XPS λΆμμ ν΅νμ¬ μ΄λ§€κ° μ±κ³΅μ μΌλ‘ μ μ‘°λ κ²μ νμΈνμλ€. λν κΈμ-λ΄μ²΄κ° μνΈμμ©μ μμ보기 μνμ¬ μΉμ¨νμ λΆμμ μννμκ³ , νμ±κΈμμ λΆμ°λμ νμ±κΈμμ νλ©΄μ μ νμΈνκΈ° μνμ¬ μμ ννν‘μ°© λΆμμ μννμλ€. κ·Έ κ²°κ³Ό, μκΈ° μ΄λ§€μ κ²½μ° κΈμ-λ΄μ²΄κ° μνΈμμ©μ΄ κ°ν μλ‘ νμ±κΈμ μ
μν¬κΈ°κ° κ°μνλ©°, λ΄μ²΄ λ΄μ κ³ λ£¨ λΆμ°λμ΄ μ΄λ§€μ νμ±μ΄ μ¦κ°νκ³ λΆν
λ° 1,3-λΆνλμμ μμ±λ λν μ¦κ°νλ κ²½ν₯μ 보μλ€. λ°λΌμ μκΈ° μ€ν κ²°κ³Όλ‘λΆν°, μ΄λ§€μ κΈμ-λ΄μ²΄κ° μνΈμμ© λ° νμ±κΈμμ νλ©΄μ κ³Ό λ°μ νμ± μ¬μ΄μ μκ΄κ΄κ³λ₯Ό κ·λͺ
νμμΌλ©° λ³Έ λ°μμ μ ν©ν μ΄λ§€ μμ€ν
μ ν립νμλ€. λν μ‘°μ΄λ§€λ‘μ κ²ν ν λ€μν κΈμλ€ μ€, Zn κΈμμ μ‘°μ΄λ§€λ‘ λμ
νμμ λ μ΄λ§€ νμ±μ΄ κ°μ₯ μ°μν¨μ νμΈνμλ€.
λ€μμΌλ‘, νμν μ‘°μ΄λ§€ μ€ κ°μ₯ νμ±μ΄ μ°μνμλ Pt/Sn/Zn/Al2O3 μ΄λ§€μμ Znμ ν¨λ λ³νκ° λ
Έλ₯΄λ§-λΆνμ μ§μ νμμν λ°μμμμ μ΄λ§€ νμ±μ λ―ΈμΉλ μν₯μ μμ보기 μν΄ Pt/Sn/XZn/Al2O3 (X=0, 0.25, 0.5, 0.75, 1.0) μ΄λ§€λ₯Ό μμ°¨μ ν¨μΉ¨λ²μ ν΅νμ¬ μ μ‘°νμλ€. XRD, ICP-AES λ° XPS λΆμμ ν΅νμ¬ μ΄λ§€κ° μ±κ³΅μ μΌλ‘ μ μ‘°λ κ²μ νμΈνμλ€. λν κΈμ-λ΄μ²΄κ° μνΈμμ©μ μμ보기 μνμ¬ μΉμ¨νμ λΆμμ μννμκ³ , νμ±κΈμμ λΆμ°λμ νμ±κΈμμ νλ©΄μ μ νμΈνκΈ° μνμ¬ μμ ννν‘μ°© λΆμμ μννμλ€. κ·Έ κ²°κ³Ό, κΈμ-λ΄μ²΄κ° μνΈμμ©κ³Ό νμ±κΈμμ λΆμ°λ λ° νλ©΄μ μ΄ λ
Έλ₯΄λ§-λΆνμ μ§μ νμμν λ°μμ μμ΄μμ λ°μ νμ±μ κ²°μ νλ μ€μν μΈμλ‘μ μμ©ν¨μ λ€μ νμΈνμλ€. μ μ‘°λ μ΄λ§€λ€ μ€μμ κ°μ₯ ν° νμ±κΈμ νλ©΄μ μ κ°μ§ Pt/Sn/0.5Zn/Al2O3 μ΄λ§€κ° λ
Έλ₯΄λ§-λΆνμ μ§μ νμμν λ°μμμ κ°μ₯ λμ μ΄λ§€νμ±μ 보μλ€.Catalysts for direct dehydrogenation of n-butane to n-butene and 1,3-butadiene were investigated in this work. A series of Pt/Sn/M/Al2O3 catalysts with different third metal (M = Li, Na, K, and Rb) were prepared by a sequential impregnation method, and they were applied to the direct dehydrogenation of n-butane to n-butene and 1,3-butadiene. Successful formation of Pt/Sn/M/Al2O3 catalysts was well confirmed by XRD, and ICP-AES measurements. Surface acidity of Pt/Sn/M/Al2O3 catalysts was measured by NH3-TPD experiments. A correlation between catalytic performance and surface acidity of Pt/Sn/M/Al2O3 catalysts revealed that the catalytic performance increased with increasing surface acidity of the catalyst. In order to decrease deactivation rate of Pt/Sn/Al2O3 catalysts, various transition metals were also investigated as a promoter of Pt/Sn/Al2O3 catalysts. A series of Pt/Sn/M/Al2O3 catalysts with different third metal (M = Zn, In, Y, Bi, and Ga) were prepared by a sequential impregnation method with a variation of promoter (M), and they were applied to the direct dehydrogenation of n-butane to n-butene and 1,3-butadiene. Successful formation of Pt/Sn/M/Al2O3 catalysts was well confirmed by XRD, ICP-AES, and XPS measurements. Metal-support interaction was measured by TPR experiments, and Pt surface area was measured by H2-chemisorption experiments, respectively, to elucidate the effect of metal-support interaction and Pt dispersion on the catalytic performance in the reaction. Amount of n-butene and 1,3-butadiene increased with increasing both metal-support interaction and Pt surface area of the catalysts. Among the catalysts tested, Pt/Sn/Zn/Al2O3 catalyst also showed the best catalytic performance in the direct dehydrogenation of n-butane. In order to investigate the effect of zinc content on the physicochemical properties and catalytic activities of Pt/Sn/Zn/Al2O3 catalysts, a series of Pt/Sn/XZn/Al2O3 catalysts with different zinc contents (X= 0, 0.25, 0.5, 0.75, and 1.0) were prepared by a sequential impregnation method with a variation of Zn content (X, wt%). Successful formation of Pt/Sn/XZn/Al2O3 catalysts was confirmed by XRD, ICP-AES, and XPS measurements. Metal-support interaction was measured by TPR experiments, and Pt surface area was measured by H2-chemisorption experiments, respectively, to elucidate the effect of metal-support interaction and Pt dispersionon the catalytic performance in the reaction. Correlationsbetween catalytic performance and TPR peak temperature, and between catalytic performance and Pt surface area revealed that the catalytic performance increased with increasing metal-support interaction and Pt surface area. Thus, both metal-support interactionand Pt surface area of the catalysts played important roles in determining the catalytic performance in the direct dehydrogenation of n-butane to n-butene and 1,3-butadiene. Among the catalysts tested, Pt/Sn/0.5Zn/Al2O3 catalyst, which retained the strongest metal-support interaction and the highest Pt surface area, showed the best catalytic performance in terms of yield for TDP and conversion of n-butane. Suitable addition of Zn (0.5 wt%) can reduce the size of the platinum ensembles by geometric effect, thus increasing the Pt surface area. However, when the content of Zn is excessive, the character of active metal has been modified by the formation of PtZn alloy and the decrease of Pt surface area is observed.1. μ λ‘
2. μ΄λ‘ λ° λ°°κ²½
2.1. λ
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λ° 1,3-λΆνλμ μ μ‘° 곡μ
2.2. νμμν λ°μ μ΄λ§€ λ° κ³΅μ
2.3. μ§μ νμμν λ°μ
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3.1. μ΄λ§€ μ μ‘°
3.1.1. μμ½
3.1.2. Pt/Sn/M/Al2O3 (M=Li, Na, K, Rb) μ΄λ§€μ μ μ‘°
3.1.3. Pt/Sn/M/Al2O3 (M=Zn, In, Y, Bi, Ga) μ΄λ§€μ μ μ‘°
3.1.4. Pt/Sn/XZn/Al2O3 (X=0, 0.25, 0.5, 0.75, 1.0) μ΄λ§€μ μ μ‘°
3.2. μ΄λ§€ νΉμ± λΆμ
3.2.1. XRD (X-Ray Diffraction)
3.2.2. ICP-AES (Inductively Coupled Plasma-Atomic Emission Spectroscopy)
3.2.3. N2 adsorption-desorption measurement
3.2.4. H2 Chemisorption
3.2.5. NH3-TPD (Temperature Programmed Desorption)
3.2.6. CHNS
3.2.7. TPR (Temperature Programmed Reduction)
3.2.8. XPS (X-ray Photoelectron Spectroscopy)
3.3. λ
Έλ₯΄λ§-λΆνμ μ§μ νμμν λ°μ
3.3.1. μ§μ νμμν λ°μ μμ€ν
3.3.2. μ΄λ§€λ₯Ό ν΅ν μ§μ νμμν λ°μμ ꡬμ±
4. μ€ν κ²°κ³Ό λ° κ³ μ°°
4.1. Pt/Sn/M/Al2O3 (M=Li, Na, K, Rb) μ΄λ§€λ₯Ό ν΅ν λ
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4.1.1. Pt/Sn/M/Al2O3 (M=Li, Na, K, Rb) μ΄λ§€μ μ μ‘° λ° κ²°μ ꡬ쑰 νμΈ
4.1.2. λ
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4.1.3. μ΄λ§€μ μ° νΉμ±κ³Ό λ°μ νμ± μ¬μ΄μ μκ΄κ΄κ³
4.2. Pt/Sn/M/Al2O3 (M=Zn, In, Y, Bi, Ga) μ΄λ§€λ₯Ό ν΅ν λ
Έλ₯΄λ§-λΆνμ μ§μ νμμν λ°μ
4.2.1. Pt/Sn/M/Al2O3 (M=Zn, In, Y, Bi, Ga) μ΄λ§€μ μ μ‘° λ° κ²°μ ꡬ쑰 νμΈ
4.2.2. λ
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4.2.4. μ΄λ§€ νμ±κΈμμ λΆμ°λ λ° νλ©΄μ κ³Ό λ°μ νμ± μ¬μ΄μ μκ΄κ΄κ³
4.2.5. νμλ μ΄λ§€μ μ μμ νΉμ±
4.2.6. μ΄λ§€μ νμ νΉμ± λ° νμ±κΈμμ νΉμ±κ³Ό λ°μ νμ± μ¬μ΄μ μκ΄κ΄κ³
4.3. Pt/Sn/XZn/Al2O3 (X=0, 0.25, 0.5, 0.75, 1.0) μ΄λ§€λ₯Ό ν΅ν λ
Έλ₯΄λ§-λΆνμ μ§μ νμμν λ°μ
4.3.1. Pt/Sn/XZn/Al2O3 (X=0, 0.25, 0.5, 0.75, 1.0) μ΄λ§€μ μ μ‘° λ° κ²°μ ꡬ쑰 νμΈ
4.3.2. λ
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4.3.4. μ΄λ§€ νμ±κΈμμ λΆμ°λ λ° νλ©΄μ κ³Ό λ°μ νμ± μ¬μ΄μ μκ΄κ΄κ³
4.3.5. νμλ μ΄λ§€μ μ μμ νΉμ±
4.3.6. μ΄λ§€μ νμ νΉμ± λ° νμ±κΈμμ νΉμ±κ³Ό λ°μ νμ± μ¬μ΄μ μκ΄κ΄κ³
5. κ²° λ‘
μ°Έκ³ λ¬Έν
AbstractMaste
An Analysis of Apartment Complexβs Main Entranceβs Architectural Expression Based on the Changes of an Apartmentβs Social Perception
Β© 2022 Architectural Institute of Korea.This study aims to analyze the transformation of architectural expressions involving an apartment complexβs main entrance and reveal the changes made on an apartmentβs social perception. An apartmentβs social perception was segmented into five distinct stages of time based on previous research that include the bourgeosify stage, commercialize stage, recession stage, differentiation stage and the intense-differentiation stage. Photographs of the architectural expressions of the main entrances were taken at actual apartment complex visits to use as data. The collected photographs were analyzed and placed into two categories: the physical forms of the main entrances and the apartment brand locations. The main entrance of an apartment complexβs physical transformation occurred when an underground parking lot was built due to the increase in vehicles and traffic in the area. For pedestrian safety, vehicle roads and pedestrian walkways were separated at the main entrance. The location of an apartment complexβs brand dramatically changed following the increase of social value regarding these brand-name apartments. Throughout the branding process, apartments showcased an ostentatious display of wealth due to the residentsβ change in social perception of apartments. During the early 2000s to early 2010s, to satisfy these architectural expression changes, gate-shaped structures were installed at a complexβs main entrance during the differentiation stage. The gate-shaped structure not only functioned to separate the vehicle and pedestrian pathways, but also served as a display of a complexβs name as their prominent ostentation strategy.N
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