2017년 4월 26일, NDC2017 발표자료입니다.
콘텐츠 제작은 게임 개발에서 많은 노력과 시간 투자를 필요로하는 작업입니다. 최근 폭발적인 관심을 받고 있는 딥러닝을 통해 여기에 드는 시간을 크게 줄일 수 있습니다. 이 발표에서는 VAE(Variational AutoEncoder)를 이용한 모방을 통한 콘텐츠 생성 기법에 대해서 다룹니다.
글로벌을 타겟으로 한 모바일 게임을 제작중인 팀 <쿡앱스>에서 만든 용어정리집 입니다. 게임회사 실무에서 실제로 사용중인 용어들을 직군 단위와(통계,마케팅,기획,그래픽,개발,사운드/영상 등) 중요도로 나누어 정리해 두었습니다.
https://www.cookapps.com/
글로벌을 타겟으로 한 모바일 게임을 제작중인 팀 <쿡앱스>에서 만든 용어정리집 입니다. 게임회사 실무에서 실제로 사용중인 용어들을 직군 단위와(통계,마케팅,기획,그래픽,개발,사운드/영상 등) 중요도로 나누어 정리해 두었습니다.
https://www.cookapps.com/
배상욱 hzael@naver.com
1. 위 내용은 애니팡 역기획입니다. 애니팡의 실제 시스템 및 기획과 전혀 상관이 없는 개인이 만든 포트폴리오입니다.
2. 역기획 내용 중 틀린 내용 및 오류가 있습니다. 너그러이 보아 주시면 감사하겠습니다.
3. 이 슬라이드의 구성 및 배경을 제외한 내용은 저의 지적재산이므로 무단 도용을 금지합니다.
4. 슬라이드의 구성은 이상균님의 게임 기획 튜토리얼의 배경을 많은 부분 참고하였습니다.
5. 슬라이드 컨버팅 중 변형된 곳이 있습니다.
6. 전리품 분배 시스템 기획 http://www.slideshare.net/SwooBae/ss-28736021
7. 전리품 분배 시스템 기획 - 아이템의 획득 DOCX 버젼 http://www.slideshare.net/SwooBae/ss-28736246
배상욱 hzael@naver.com
1. 위 내용은 애니팡 역기획입니다. 애니팡의 실제 시스템 및 기획과 전혀 상관이 없는 개인이 만든 포트폴리오입니다.
2. 역기획 내용 중 틀린 내용 및 오류가 있습니다. 너그러이 보아 주시면 감사하겠습니다.
3. 이 슬라이드의 구성 및 배경을 제외한 내용은 저의 지적재산이므로 무단 도용을 금지합니다.
4. 슬라이드의 구성은 이상균님의 게임 기획 튜토리얼의 배경을 많은 부분 참고하였습니다.
5. 슬라이드 컨버팅 중 변형된 곳이 있습니다.
6. 전리품 분배 시스템 기획 http://www.slideshare.net/SwooBae/ss-28736021
7. 전리품 분배 시스템 기획 - 아이템의 획득 DOCX 버젼 http://www.slideshare.net/SwooBae/ss-28736246
▶ 스마트스터디 채용안내
https://goo.gl/Ondwo8
▶ 발표자 소개
현재 수집형 RPG [몬스터슈퍼리그] 시스템기획 및 라이브기획PM.
법학과 졸업 후 게임업계로 와서 [디스코판다 for kakao], [LINE 플러피다이버], [범핑베어 with facebook] 등 각종 플랫폼과 국가에서의 게임상용화 경험을 토대로 글로벌원빌드 [몬스터슈퍼리그]에 열심히 녹여내는 중.
▶ 발표자료 소개
게임지표 중 누구에게나 다루기 어려운 난제, 리텐션(Retention).
[몬스터슈퍼리그] 글로벌서비스를 하며 리텐션을 15% 개선한 놀라운 경험을 바탕으로, '리텐션을 개선하려면 수치를 조정해야 한다?', '더 좋은 보상을 줘야 한다?'와 같은 숫자 위주 판단에서 벗어나 감성적이지만 파괴력 있는, 조금은 낯설지만 솔깃한 팁을 공유합니다.
※ 실제로 라이브에 적용했던 내용을 상세히 공유하려고 합니다. 최대한 현실작업과 가까운 형태로 기획자의 머릿속을 들여다 보는 시간이 되기를 바랍니다.
SMARTSTUDY 에서 몬스터 슈퍼 리그를 개발하면서 빠른 개발 진행을 위해 선택했던 Python 게임 서버, '잘 되면 다시 만들지 뭐'라는 생각에서 시작했지만 다시 만들 일은 영원히 오지 않았습니다... Python으로 게임 서버를 만들었을 때 사용한 것은 무엇인지 또 실제 오픈 했을 때 서버는 안녕했는지 알아봅니다.
추석에 새기는 마음, '오래 오래 건강하게'
둥근 보름달만큼이나 마음도 넉넉해지는 한가위입니다.
자연의 혜택을 느긋하게 누리는 풍요의 시간,
소중한 이들에게 사랑과 감사의 마음을 전하는 것이 우리네 미덕이지요.
일백년 한결같은 정성으로 명품을 완성해온 정관장으로
그 애틋한 마음을 건네보세요.
2013년 정관장 추석선물 카탈로그 입니다.
With cheap cameras becoming ubiquitous the camera has become probably the most
important sensor for many applications.
However extracting usable information from the images produced by cameras is
non-trivial. There have been many published successes in recent years using deep
learning (multi-layered convolutional neural networks) but it’s not always
necessary to apply such techniques to get useful results for many applications.
This talk will focus on “classical” machine vision using java and the OpenCV
library. We’ll start with a quick refresher on how image data is represented and
then cover topics such as determining if an image is blurred (and therefore
unusable) and then explore a number of techniques such as shape and face
detection.
When Node.js Goes Wrong: Debugging Node in Production
The event-oriented approach underlying Node.js enables significant concurrency using a deceptively simple programming model, which has been an important factor in Node's growing popularity for building large scale web services. But what happens when these programs go sideways? Even in the best cases, when such issues are fatal, developers have historically been left with just a stack trace. Subtler issues, including latency spikes (which are just as bad as correctness bugs in the real-time domain where Node is especially popular) and other buggy behavior often leave even fewer clues to aid understanding. In this talk, we will discuss the issues we encountered in debugging Node.js in production, focusing upon the seemingly intractable challenge of extracting runtime state from the black hole that is a modern JIT'd VM.
We will describe the tools we've developed for examining this state, which operate on running programs (via DTrace), as well as VM core dumps (via a postmortem debugger). Finally, we will describe several nasty bugs we encountered in our own production environment: we were unable to understand these using existing tools, but we successfully root-caused them using these new found abilities to introspect the JavaScript VM.
Similar to [NDC2017] 딥러닝으로 게임 콘텐츠 제작하기 - VAE를 이용한 콘텐츠 생성 기법 연구 사례 (20)
Automatic Generation of Game Content using a Graph-based Wave Function Collap...Hwanhee Kim
This paper describes a procedural content generation system based on a non-grid wave function collapse (WFC) algorithm. The goal of this system is for the game designer procedurally to create key content elements in the game level through simple association rule input. To do this, we proposed a graph-based data structure that can be easily integrated with a navigation mesh data structure in a 3D world. With our system, if the user inputs the minimum association rule, it is possible effectively to generate PCG content in the three-dimensional world. The experimental results show that the WFC, which is a texture synthesis algorithm, can be extended to a non-grid shape with high control ability and scalability.
2019년 4월 27일에 있었던 한국게임학회 인공지능분과 두번째 모임에 진행했던 <Tensorflow 2.0 튜토리얼 - RNN> 강연자료입니다.
RNN 을 처음 접하는 분들을 위해 RNN 의 기본 개념을 짚어보며, Google Colab Sample Code 를 통해 2.0 에서 RNN 을 사용하는 방법을 간단하게 살펴봅니다.
2019년 4월 27일에 있었던 한국게임학회 인공지능분과 두번째 모임에 진행했던 <Tensorflow 2.0 튜토리얼 - CNN> 강연자료입니다. CNN 을 처음 접하는 분들을 위해 CNN 의 기본 개념을 짚어보며, Google Colab Sample Code 를 통해 2.0 에서 CNN 을 사용하는 방법을 간단하게 살펴봅니다.
2019년 3월 23일에 있었던 한국게임학회 인공지능분과 첫 모임에 진행했던 <구글 텐서플로우 첫걸음> 강연자료입니다. 텐서플로우 소개와 2.0 에 대한 간략한 특징을 다루고 있고, Sample Code 를 통해 2.0 에 추가된 feature 들을 간단히 다뤄봅니다.
Sample Code link : https://colab.research.google.com/drive/1CODzwAU5BE9h99s-xJZCb9B__UHXpucE
한국게임학회 게임인공지능 분과
2019년 스터디 모임 준비자료입니다.
이 정보는 2019년 3월 1일 기준입니다.
설치에 사용된 프로그램 버전은 아래와 같습니다.
Windows 10
python 3
CUDA 9.0
cuDNN 7.5
tensorflow-gpu 1.10.0
2016년 4월 27일, NDC2016 발표자료입니다.
전통적인 콘텐츠 생산은 기획자, 개발자가 하나하나 컨트롤해야 하는 노동집약적 산업입니다. 저 또한 처음에 입사했을 때 출시를 위해 며칠 만에 이백여 개가 넘는 맵을 찍어야 했던 아픔이 있습니다. 그 뒤에도 플레이어들을 위한 콘텐츠 생산은 계속되었지만, 플레이어들의 콘텐츠 소비 속도는 생산 속도를 뛰어넘은 지 오래되었습니다. 대안은 로그라이크 같은 장르에서 널리 쓰이는 Procedural Contents Generation이라고 생각합니다. 외국 인디 씬에서는 이미 많이 사용되고 있는 이 방법에 최근 점점 더 많이 사용되고 있는 신경망을 활용해서 색다른 콘텐츠 생산 기법을 소개하려 합니다.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
9. 기술의 빠른 발전
• <Unsupervised Representation Learning with Deep Convolutional
Generative Adversarial Networks>
https://www.semanticscholar.org/paper/Unsupervised-Representation-Learning-with-Deep-Radford-
Metz/35756f711a97166df11202ebe46820a36704ae77
10. 기술의 빠른 발전
• <BEGAN: Boundary Equilibrium Generative Adversarial Networks>
https://github.com/Heumi/BEGAN-tensorflow
11. 기술의 빠른 발전
• <BEGAN: Boundary Equilibrium Generative Adversarial Networks>
https://github.com/Heumi/BEGAN-tensorflow
13. 딥러닝을 이용한 생성 모델
• VAE (Variational Auto-Encoder)
• GAN (Generative Adversarial Networks)
• PixelRNN
https://blog.openai.com/generative-models/
14. GAN (Generative Adversarial
Networks)• 적대적 생성 모델
• 이미지를 생성하는 생성자(Generator, G)와 이미지의 진짜/가짜를 판단하는
판별자(Discriminator, D)가 상호 경쟁하며 발전
https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f
28. 이미지의 차이를 줄이는 쪽으로 학습
• 이미지의 차이(error) = MSE = Mean Squared Error
https://blog.insightdatascience.com/isee-removing-eyeglasses-from-faces-using-deep-learning-d4e7d935376f
33. 압축된 정보 = latent variable
https://www.slideshare.net/TJTorres1/deep-style-using-variational-autoencoders-for-image-generation
34. Unit Gaussian → latent variable
• 평균μ, 분산σ2, 표준편차σ를 가지는 분포의 형태
https://www.slideshare.net/TJTorres1/deep-style-using-variational-autoencoders-for-image-generation
43. 4. 생성 사례
- Dungeon Shape, 캐릭터 Portrait, Sprite 등
44. 개발 환경
• Tensorflow v1.0.1 (windows 10)
• Python 3.5
• GTX1070
• Javascript (Web Demo)
45. Network Overview
• VAE Network + recurrent generation
• Pooling Layer 대신 Convolution Layer 에서 stride=2 (2칸씩 건너뛰기)
• Kevin frans 의 코드 참고
http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html
94. Analogy
• 유추
• A : B = C : ?
• ? = (B + C) – A
<Sampling Generative Networks>, Tom White
J-Diagram
http://www.nibcode.com/en/psychometric-training/abstract-reasoning-test/
108. Experiment
3rd frame +3rd frame
mean
+3rd frame
mean
+3rd frame
mean
+3rd frame
mean
+3rd frame
mean
+3rd frame
mean
-3rd frame
mean
-3rd frame
mean
-3rd frame
mean
-3rd frame
mean
-3rd frame
mean
-3rd frame
mean
110. 결론 & 논의
• 레벨 디자인 같은 데이터는 실사용 가능
• 이미지 데이터는 실사용 무리 → 멀지 않은 미래에 가능할 듯 (BEGAN 등)
• Original 데이터는 많을수록 좋음
• 이 방법으로는 최소 N > 2,000
• Latent variable 탐색 방법은 발전의 여지가 많음
