Ying Huang

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<channel>
	<title>Ying Huang</title>
	<link>https://yunyingh.com</link>
	<description>Ying Huang</description>
	<pubDate>Thu, 18 May 2023 09:03:41 +0000</pubDate>
	<generator>https://yunyingh.com</generator>
	<language>en</language>
	
		
	<item>
		<title>Miss Dior Blooming Bouquet</title>
				
		<link>https://yunyingh.com/Miss-Dior-Blooming-Bouquet</link>

		<pubDate>Thu, 18 May 2023 08:36:03 +0000</pubDate>

		<dc:creator>Ying Huang</dc:creator>

		<guid isPermaLink="true">https://yunyingh.com/Miss-Dior-Blooming-Bouquet</guid>

		<description>&#38;nbsp;
Miss Dior Blooming Bouquet Social Video

Art Director of MDBB social video project


&#60;img width="1296" height="2304" width_o="1296" height_o="2304" data-src="https://freight.cargo.site/t/original/i/8817704b4c0f11872dc571c9df245af41ec24baef0c945ab7e736193754e5789/-copy.jpg" data-mid="179155374" border="0"  src="https://freight.cargo.site/w/1000/i/8817704b4c0f11872dc571c9df245af41ec24baef0c945ab7e736193754e5789/-copy.jpg" /&#62;
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&#60;img width="1296" height="2304" width_o="1296" height_o="2304" data-src="https://freight.cargo.site/t/original/i/4b5822efc6fa2618621f49021304171cc5c5d39f4245f5bcc4eaccbf6d71a2ed/-copy.jpg" data-mid="179155375" border="0"  src="https://freight.cargo.site/w/1000/i/4b5822efc6fa2618621f49021304171cc5c5d39f4245f5bcc4eaccbf6d71a2ed/-copy.jpg" /&#62;


Index &#38;nbsp; &#38;nbsp;Next︎
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	<item>
		<title>WallpaperSTORE AR experience</title>
				
		<link>https://yunyingh.com/WallpaperSTORE-AR-experience</link>

		<pubDate>Thu, 18 May 2023 09:03:41 +0000</pubDate>

		<dc:creator>Ying Huang</dc:creator>

		<guid isPermaLink="true">https://yunyingh.com/WallpaperSTORE-AR-experience</guid>

		<description>&#38;nbsp;
WallpaperSTORE AR experience

Created five groups of AR sceneshttps://www.behance.net/gallery/164549679/Violet-DreamCatcher

&#60;img width="2400" height="1920" width_o="2400" height_o="1920" data-src="https://freight.cargo.site/t/original/i/f32f2feee2ddace381e26246c390c4eb267178061e5fa31b722c7de966899eb2/4_1.jpg" data-mid="179156692" border="0"  src="https://freight.cargo.site/w/1000/i/f32f2feee2ddace381e26246c390c4eb267178061e5fa31b722c7de966899eb2/4_1.jpg" /&#62;
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&#60;img width="852" height="1545" width_o="852" height_o="1545" data-src="https://freight.cargo.site/t/original/i/73cf6c0d706617913d710592ac960f5dff313929496baa0bcbda9226808dd82f/1.png" data-mid="179156671" border="0"  src="https://freight.cargo.site/w/852/i/73cf6c0d706617913d710592ac960f5dff313929496baa0bcbda9226808dd82f/1.png" /&#62;
&#60;img width="852" height="1545" width_o="852" height_o="1545" data-src="https://freight.cargo.site/t/original/i/4460813c5f5a0d1a2fb5ae476ab6cca8c1c7a51e3c9f6a2a2ec12e8e208d0e58/3.png" data-mid="179156673" border="0"  src="https://freight.cargo.site/w/852/i/4460813c5f5a0d1a2fb5ae476ab6cca8c1c7a51e3c9f6a2a2ec12e8e208d0e58/3.png" /&#62;
Index &#38;nbsp; &#38;nbsp;Next︎
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		<title>Generative Type</title>
				
		<link>https://yunyingh.com/Generative-Type</link>

		<pubDate>Thu, 18 May 2023 08:47:47 +0000</pubDate>

		<dc:creator>Ying Huang</dc:creator>

		<guid isPermaLink="true">https://yunyingh.com/Generative-Type</guid>

		<description>&#38;nbsp;
Generative Type Site

Worked with Ivan Cruz for a real-time generative type website.https://symphosizer.wearecollins.com/


&#60;img width="5384" height="3715" width_o="5384" height_o="3715" data-src="https://freight.cargo.site/t/original/i/262b8b11194be283dc43041c99ab8f128b099929bf6369e4add15004e3193538/sympozier-min-copy-min.jpg" data-mid="179155615" border="0"  src="https://freight.cargo.site/w/1000/i/262b8b11194be283dc43041c99ab8f128b099929bf6369e4add15004e3193538/sympozier-min-copy-min.jpg" /&#62;&#60;img width="6000" height="4500" width_o="6000" height_o="4500" data-src="https://freight.cargo.site/t/original/i/4b039f7724d618bf2cc9833dccaf0820c50fde1087c6ca3a6ec062a809162679/symposizer2-min.jpg" data-mid="179155614" border="0"  src="https://freight.cargo.site/w/1000/i/4b039f7724d618bf2cc9833dccaf0820c50fde1087c6ca3a6ec062a809162679/symposizer2-min.jpg" /&#62;Index &#38;nbsp; &#38;nbsp;Next︎
</description>
		
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	<item>
		<title>Ying x RayBan</title>
				
		<link>https://yunyingh.com/Ying-x-RayBan</link>

		<pubDate>Mon, 15 Mar 2021 05:04:50 +0000</pubDate>

		<dc:creator>Ying Huang</dc:creator>

		<guid isPermaLink="true">https://yunyingh.com/Ying-x-RayBan</guid>

		<description>&#38;nbsp;
Ying x RayBan 2021&#60;img width="6500" height="4000" width_o="6500" height_o="4000" data-src="https://freight.cargo.site/t/original/i/4616a15a54be3efaadf56e2136aeb7832be5477e5384337064f5b92a0e91f51e/rayban2-min.jpg" data-mid="179149260" border="0"  src="https://freight.cargo.site/w/1000/i/4616a15a54be3efaadf56e2136aeb7832be5477e5384337064f5b92a0e91f51e/rayban2-min.jpg" /&#62;&#60;img width="6500" height="4000" width_o="6500" height_o="4000" data-src="https://freight.cargo.site/t/original/i/b287512952787b7601b5fa3aa9d8101ac2f6fa5ec7b51343bc77505ad0762caa/rayban1-copy.jpg" data-mid="179149267" border="0"  src="https://freight.cargo.site/w/1000/i/b287512952787b7601b5fa3aa9d8101ac2f6fa5ec7b51343bc77505ad0762caa/rayban1-copy.jpg" /&#62;


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&#60;img width="3600" height="3600" width_o="3600" height_o="3600" data-src="https://freight.cargo.site/t/original/i/e82b696ea2eec86d17ac19e7fc45ade47c1bb0d08e7a4d2060a5f5aa994086f6/IMG_8067.JPG" data-mid="102040311" border="0"  src="https://freight.cargo.site/w/1000/i/e82b696ea2eec86d17ac19e7fc45ade47c1bb0d08e7a4d2060a5f5aa994086f6/IMG_8067.JPG" /&#62;
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&#60;img width="3600" height="3600" width_o="3600" height_o="3600" data-src="https://freight.cargo.site/t/original/i/f7e33c36b9d46792a46edce1734057f3e8c1408099de001d809b2e11667ee29d/IMG_8069.JPG" data-mid="102040313" border="0"  src="https://freight.cargo.site/w/1000/i/f7e33c36b9d46792a46edce1734057f3e8c1408099de001d809b2e11667ee29d/IMG_8069.JPG" /&#62;
&#60;img width="3600" height="3600" width_o="3600" height_o="3600" data-src="https://freight.cargo.site/t/original/i/da9a7cdb2f37f714f3b0a02973ec5eb711512f5a06c64746503e02fd07f3089d/IMG_8064.JPG" data-mid="102040308" border="0"  src="https://freight.cargo.site/w/1000/i/da9a7cdb2f37f714f3b0a02973ec5eb711512f5a06c64746503e02fd07f3089d/IMG_8064.JPG" /&#62;


Index &#38;nbsp; &#38;nbsp;Next︎
</description>
		
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	<item>
		<title>AI Knitwear</title>
				
		<link>https://yunyingh.com/AI-Knitwear</link>

		<pubDate>Wed, 04 Sep 2019 23:10:21 +0000</pubDate>

		<dc:creator>Ying Huang</dc:creator>

		<guid isPermaLink="true">https://yunyingh.com/AI-Knitwear</guid>

		<description>Al Knitwear


&#60;img width="4000" height="6000" width_o="4000" height_o="6000" data-src="https://freight.cargo.site/t/original/i/51389bf0abb05bdc5fcd4e2f22e3aa3718939cc95477915a9ad7dce57552badd/DSCF1196.jpg" data-mid="49917740" border="0"  src="https://freight.cargo.site/w/1000/i/51389bf0abb05bdc5fcd4e2f22e3aa3718939cc95477915a9ad7dce57552badd/DSCF1196.jpg" /&#62;
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Photo credit: @anthonyespinostudio


Post-graduate Fellowship project

Time:&#38;nbsp;July 2019 - Dec 2019
Part of the summer research project SAMPLER led by Elise Co (The&#38;nbsp; earlier AI pattern research part)

Responsibility and Workflow:

- Concept development : Created a series of knitwear that encodes algorithmic messages generated by machine learning to illustrate a brand new knitted fashion aesthetic.- Research :&#38;nbsp; Researched and experimented on different machine learning algorithm to identify the most suitable one; researched on the current mechanism and limitations of machine knitting, speculated the direction of future of knitting.- Coding and prototyping :&#38;nbsp; Integrated generative machine learning algorithms on custom knitting pattern datasets; built a program to facilitate this new way of knitting.- Making : Knitted the swatches on Brother KH940, Brother Bulky both single beds and double beds using different techniques. Used the swatch to make a finished garment.- Post-production :&#38;nbsp;Photography and branding for dissemination and documentation of the AI knitwear fashion line.

INTRO&#38;nbsp;AI knitwear explores the possibility of integrating machine learning algorithms with traditional knitting techniques by examining the impact of a machine generated patterns and aesthetics on our everyday wearables – garments. Due to the glitchy nature of the AI generative patterns, the project is a challenge to current knitting techniques in both mechanisms and aesthetics.&#38;nbsp;
BACKGROUND &#38;amp; RESEARCH
1. Knitting Machines:
How a knitting machine looks like and how it produces fabric(you can add a motor to the carriage or hand push it to knit):

&#60;img width="1280" height="960" width_o="1280" height_o="960" data-src="https://freight.cargo.site/t/original/i/8b9fb95b346103ea692b89c894fac2734d910897d8f5b4a564f64963ad6d5b36/kk93-1.jpg" data-mid="50340162" border="0"  src="https://freight.cargo.site/w/1000/i/8b9fb95b346103ea692b89c894fac2734d910897d8f5b4a564f64963ad6d5b36/kk93-1.jpg" /&#62;
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&#60;img width="466" height="311" width_o="466" height_o="311" data-src="https://freight.cargo.site/t/original/i/5af89aac62969a9867bbdc2cd09a19e7777b8c2247326aa7c51967ee26c74399/giphy.gif" data-mid="50340161" border="0"  src="https://freight.cargo.site/w/466/i/5af89aac62969a9867bbdc2cd09a19e7777b8c2247326aa7c51967ee26c74399/giphy.gif" /&#62;
&#60;img width="830" height="720" width_o="830" height_o="720" data-src="https://freight.cargo.site/t/original/i/12a218b5f46b31b7a8210803c5907efbdb96190dde324bad1a165f640425fc68/knitting_machine.gif" data-mid="50340163" border="0"  src="https://freight.cargo.site/w/830/i/12a218b5f46b31b7a8210803c5907efbdb96190dde324bad1a165f640425fc68/knitting_machine.gif" /&#62;
Image copyright:(From top left to bottom) Goodey’s knitting toys, OurMakerLife Blog, GIPHY, Claire Williams 
&#38;nbsp;Limitations of the current home knitting machine:
a. Width: Limited to the width of the knitting machine bed.b. MultiColor:Limited to 2 colors for single bed knitting, 4 colors for double bed knitting. For home knitting machines even with a color changer integrated 4 color is usually the maximum. The more color that is used the more floats in the back which results in an uneven surface for single bed fabric, or a more stiff surface for double bed fabric, which is bad for a fabric.&#38;nbsp;
c. Customized pattern: Not really accessible; it takes lots of time and effort, or money. 
d. Intarsia: &#38;nbsp;A technique used to knit multi-color pattern work. However, it can only apply a  block of color instead of single scattered pixels of color. (see image below).e. Open-source knitting machine hacking programs(e.g. AYAB): only support up to 6 colors for one pattern and with substandard performance for knitting 6 colors.


&#60;img width="2402" height="738" width_o="2402" height_o="738" data-src="https://freight.cargo.site/t/original/i/028b025b7a049f400bd842899bff3d960ed4b9a992fc50bed9561f87ca2758bb/intarsia.jpg" data-mid="50042862" border="0" data-scale="94" alt="Left: blocks of color V.S. Right: scattered pixels of colors" data-caption="Left: blocks of color V.S. Right: scattered pixels of colors" src="https://freight.cargo.site/w/1000/i/028b025b7a049f400bd842899bff3d960ed4b9a992fc50bed9561f87ca2758bb/intarsia.jpg" /&#62;
&#38;nbsp;Left: blocks of color(image from @annieleelarson) V.S. Right: scattered pixels of colors
Opportunities for innovation: 
a. A multicolor changer that supports unlimited number of colors (may need disassemble and reassemble). 
b. A program that connects with the machine and supports unlimited number of colors. 
c. A machine/ carriage that knits scattered multicolor work without tons of floats at the back (single bed) and/or makes the fabric very stretchy or stiff (double bed).
&#38;nbsp;

2. Knitting patterns &#38;amp;AI algorithms: what a knitting pattern chart looks like:

&#60;img src="https://i.pinimg.com/originals/99/3f/a1/993fa18d92d58a56407b122eb63600ad.jpg"&#62;A knitting pattern chart is simply pixel colors on a grid. One square represents one stitch on the swatch. If the swatch is bigger than the dimension of the chart, for example, the swatch is 100 needles wide by 100 rows, but the pattern chart is 10 by 10, the pattern will repeat itself 10 times per row. With the pattern chart, a knitter can knit the charts with multicolor or a structural pattern such as lace.&#38;nbsp;

Example (images from Internet):2-color checkerboard swatch V.S. checkerboard lace pattern swatch

&#60;img width="450" height="321" width_o="450" height_o="321" data-src="https://freight.cargo.site/t/original/i/1a3e5eed8d4f0fcdfab79684eaf1533352593b96beed7b0c97c84ced3384b6db/slipstitch-592231463df78cf5fae3fec1.jpeg" data-mid="50335992" border="0"  src="https://freight.cargo.site/w/450/i/1a3e5eed8d4f0fcdfab79684eaf1533352593b96beed7b0c97c84ced3384b6db/slipstitch-592231463df78cf5fae3fec1.jpeg" /&#62;
&#60;img width="300" height="300" width_o="300" height_o="300" data-src="https://freight.cargo.site/t/original/i/1bae270a2b06604ca9c9b1d294eceacbbae7a74c18f6c5689e42fd45952f5230/checkerboard-lace.jpg" data-mid="50335991" border="0"  src="https://freight.cargo.site/w/300/i/1bae270a2b06604ca9c9b1d294eceacbbae7a74c18f6c5689e42fd45952f5230/checkerboard-lace.jpg" /&#62;
From the example above, we can see that using different knitting techniques, one pattern chart can produce very different fabrics. This is one of the things that I am fascinated by knitting, it’s like computer graphics and after effects, but instead of digital post-production, this is a physical post-production. With different combination of yarns, gauge, technique, single or double side fabric, you have the ability to create unlimited physical effects on the same digital image.

After I spent days collecting various knitting pattern charts for my AI training dataset, I built a fundamental understanding of those patterns and started to categorize them.
︎Traditional patterns&#38;nbsp; &#38;nbsp; &#38;nbsp;&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/bccdcc83f1b1d4bd96617c0eaad6017bcedc5dccd5a3cac985a23cc2c7a66391/128__0079_book156.jpg" data-mid="50336801" border="0"  src="https://freight.cargo.site/w/128/i/bccdcc83f1b1d4bd96617c0eaad6017bcedc5dccd5a3cac985a23cc2c7a66391/128__0079_book156.jpg" /&#62;

︎Figurative patterns
&#38;nbsp; &#38;nbsp; &#38;nbsp;&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/b2169a367cd8ba251864c10bfdf48cecce24b3ddcc4614d5cbfdc000bd059b77/128__0104_book131.jpg" data-mid="50336791" border="0"  src="https://freight.cargo.site/w/128/i/b2169a367cd8ba251864c10bfdf48cecce24b3ddcc4614d5cbfdc000bd059b77/128__0104_book131.jpg" /&#62;

︎Geometric patterns
&#38;nbsp; &#38;nbsp; &#38;nbsp;&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/d84a728033e6902dc7dcfcdbbda78cfdc5161a5e821e7bbe26e0b7bbdaf50dfd/fliped_horiz_0023_book245.jpg" data-mid="50336958" border="0"  src="https://freight.cargo.site/w/128/i/d84a728033e6902dc7dcfcdbbda78cfdc5161a5e821e7bbe26e0b7bbdaf50dfd/fliped_horiz_0023_book245.jpg" /&#62;
︎Abstract patterns

&#38;nbsp; &#38;nbsp; &#38;nbsp;&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/d32f8036d5ce073a15f42824fa512aeb2266ca4acc979265dda1bef844b74372/fliped_horiz_0062_book173.jpg" data-mid="50336959" border="0"  src="https://freight.cargo.site/w/128/i/d32f8036d5ce073a15f42824fa512aeb2266ca4acc979265dda1bef844b74372/fliped_horiz_0062_book173.jpg" /&#62;
Traditional patterns are usually those we see often on a sweater from the 90s while figurative and geometric patterns are very much self-explanatory. I found abstract patterns are the most interesting and have the most potential to be surprising, because they are usually unexpected. Like machine learning algorithms you never know what features the algorithm decides to pick up and why they “think” the result it generates is as real as the human generated one.

After gaining enough knowledge and hands-on knitting experience, I am moved to pattern generation, analysis, design, and final product production.
ALGORITHM &#38;amp; GENERATIVE PATTERNS
For knitting pattern generation, I used an algorithm called GAN(Generative Adversarial Network). The algorithm learns the feature like color, texture, composition of the input images, mimic it, then output a set of images that it thinks is as real as the real input. 
I experimented with 3 different GAN algorithms and 5 different datasets, here are some results generated by algorithms:
1. Trained with multi-colored pattern charts


&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/0d24a9ea70e9fb3f198643c27f3a83ceac075a25f0d222ea497b5e43028a1a6c/OUTying_new1test_arange_5_3.png" data-mid="50342911" border="0"  src="https://freight.cargo.site/w/128/i/0d24a9ea70e9fb3f198643c27f3a83ceac075a25f0d222ea497b5e43028a1a6c/OUTying_new1test_arange_5_3.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/1277e293137d3ea3c86e81429297d449b919b39875357ed24723e4d264c5601b/OUTying_new1test_arange_6_7.png" data-mid="50342915" border="0"  src="https://freight.cargo.site/w/128/i/1277e293137d3ea3c86e81429297d449b919b39875357ed24723e4d264c5601b/OUTying_new1test_arange_6_7.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/9e862d8eaf387f7c47a7ecb301c65e100238993424105c0f68eb064671b53bb0/OUTying_new1test_arange_5_6.png" data-mid="50342914" border="0"  src="https://freight.cargo.site/w/128/i/9e862d8eaf387f7c47a7ecb301c65e100238993424105c0f68eb064671b53bb0/OUTying_new1test_arange_5_6.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/9053889a216b579f037b5809ddf50421a64b6a684c696c314aecc296bb546008/OUTying_new1test_arange_5_5.png" data-mid="50342913" border="0"  src="https://freight.cargo.site/w/128/i/9053889a216b579f037b5809ddf50421a64b6a684c696c314aecc296bb546008/OUTying_new1test_arange_5_5.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/f47eb3ede3f300aad26e6ca6432764dc3366b08ae2ff24e9ad1ac5209bf415fe/OUTying_new1test_arange_5_4.png" data-mid="50342912" border="0"  src="https://freight.cargo.site/w/128/i/f47eb3ede3f300aad26e6ca6432764dc3366b08ae2ff24e9ad1ac5209bf415fe/OUTying_new1test_arange_5_4.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/6f57eabc043734d95f057a25865490d793659ded973a031b7ebdd853fe784501/OUTying_new1test_arange_5_1.png" data-mid="50342909" border="0"  src="https://freight.cargo.site/w/128/i/6f57eabc043734d95f057a25865490d793659ded973a031b7ebdd853fe784501/OUTying_new1test_arange_5_1.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/e34bbedf40c7bfdd92646b2325b09a0726652d45b6131b92912e7cadb72f207b/OUTying_new1test_arange_5_0.png" data-mid="50342908" border="0"  src="https://freight.cargo.site/w/128/i/e34bbedf40c7bfdd92646b2325b09a0726652d45b6131b92912e7cadb72f207b/OUTying_new1test_arange_5_0.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/34029744add8c5a17f40d59e3b8d4b6581320d8e24ddfb39bbe82c3c30369918/OUTying_new1test_arange_5_2.png" data-mid="50342910" border="0"  src="https://freight.cargo.site/w/128/i/34029744add8c5a17f40d59e3b8d4b6581320d8e24ddfb39bbe82c3c30369918/OUTying_new1test_arange_5_2.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/6204b966c3b774c857cb3b4c1821eee3dbc7aa25fa45d761f9c95c1c82ef4309/OUTying_new1test_arange_6_8.png" data-mid="50342916" border="0"  src="https://freight.cargo.site/w/128/i/6204b966c3b774c857cb3b4c1821eee3dbc7aa25fa45d761f9c95c1c82ef4309/OUTying_new1test_arange_6_8.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/4e5479a7b0b4948a0c786fe281c8a0fb0db877b4734d55ea34e6c59e6c7fe3d0/OUTying_new1test_arange_5_0.png" data-mid="50342932" border="0"  src="https://freight.cargo.site/w/128/i/4e5479a7b0b4948a0c786fe281c8a0fb0db877b4734d55ea34e6c59e6c7fe3d0/OUTying_new1test_arange_5_0.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/09ee18a8aaecfe3563c222d9a1bca5660b60c93165adf537cd65bbdebe6393a0/OUTying_new1test_arange_6_10.png" data-mid="50342918" border="0"  src="https://freight.cargo.site/w/128/i/09ee18a8aaecfe3563c222d9a1bca5660b60c93165adf537cd65bbdebe6393a0/OUTying_new1test_arange_6_10.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/5c4fc2fe06a1dc275af365479d8b126e5ae7b52507647ea296bef93da2af3e37/OUTying_new1test_arange_6_11.png" data-mid="50342919" border="0"  src="https://freight.cargo.site/w/128/i/5c4fc2fe06a1dc275af365479d8b126e5ae7b52507647ea296bef93da2af3e37/OUTying_new1test_arange_6_11.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/a87be1221bb4f07e737f008bef6b13b20de9114528584619b39d84a0797518c1/OUTying_new1test_arange_6_12.png" data-mid="50342920" border="0"  src="https://freight.cargo.site/w/128/i/a87be1221bb4f07e737f008bef6b13b20de9114528584619b39d84a0797518c1/OUTying_new1test_arange_6_12.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/acd1476f45e8c111d44844375c7e423c9955a35984bda21dc44fac26a95a0387/OUTying_new1test_arange_7_14.png" data-mid="50342922" border="0"  src="https://freight.cargo.site/w/128/i/acd1476f45e8c111d44844375c7e423c9955a35984bda21dc44fac26a95a0387/OUTying_new1test_arange_7_14.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/585deb5f62f8f22bff34819267e0f1c0db3f594622011fd923cc1c25bea8748b/OUTying_new1test_arange_7_15.png" data-mid="50342923" border="0"  src="https://freight.cargo.site/w/128/i/585deb5f62f8f22bff34819267e0f1c0db3f594622011fd923cc1c25bea8748b/OUTying_new1test_arange_7_15.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/92f1674b5aa2b51c77899dcfc34c012c118bba9968b55e412cad1e108c0b8fa8/OUTying_new1test_arange_7_16.png" data-mid="50342924" border="0"  src="https://freight.cargo.site/w/128/i/92f1674b5aa2b51c77899dcfc34c012c118bba9968b55e412cad1e108c0b8fa8/OUTying_new1test_arange_7_16.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/a6b0aa113f552ff53203f6fb333b6352305c7eb68c95225ecc32f9f2e446fba6/OUTying_new1test_arange_7_17.png" data-mid="50342925" border="0"  src="https://freight.cargo.site/w/128/i/a6b0aa113f552ff53203f6fb333b6352305c7eb68c95225ecc32f9f2e446fba6/OUTying_new1test_arange_7_17.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/d983c0b53e2f3b51718ddc807fdbcf6e4e121e24cb6336125b810c01121d562e/OUTying_new1test_arange_7_18.png" data-mid="50342926" border="0"  src="https://freight.cargo.site/w/128/i/d983c0b53e2f3b51718ddc807fdbcf6e4e121e24cb6336125b810c01121d562e/OUTying_new1test_arange_7_18.png" /&#62;


2. Trained with only black and white pattern charts

&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/b3b3ae3754c11f7de7a73dc62834816d54ce58f153cc364568a062fd40af188d/OUTying_test_arange_0_0.png" data-mid="50341682" border="0"  src="https://freight.cargo.site/w/128/i/b3b3ae3754c11f7de7a73dc62834816d54ce58f153cc364568a062fd40af188d/OUTying_test_arange_0_0.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/f978dcaf14ba3e806f584d674087e0bb50c60c1539767272a77a35ecdb717ac8/OUTying_test_arange_0_2.png" data-mid="50341685" border="0"  src="https://freight.cargo.site/w/128/i/f978dcaf14ba3e806f584d674087e0bb50c60c1539767272a77a35ecdb717ac8/OUTying_test_arange_0_2.png" /&#62;
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&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/dbd14e698004e8b9a55a7324c1d2727b3068c64002bbfc1069cf01cb5ae3a701/OUTying_test_arange_1_14.png" data-mid="50341697" border="0"  src="https://freight.cargo.site/w/128/i/dbd14e698004e8b9a55a7324c1d2727b3068c64002bbfc1069cf01cb5ae3a701/OUTying_test_arange_1_14.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/5c08f636b8f750d575f6e7d1e87cb6e3ac94e461c543537d3eb835463e31fa64/OUTying_test_arange_2_16.png" data-mid="50341831" border="0"  src="https://freight.cargo.site/w/128/i/5c08f636b8f750d575f6e7d1e87cb6e3ac94e461c543537d3eb835463e31fa64/OUTying_test_arange_2_16.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/b64bcff8fb781bc7ab6a7bb50fd8336b1d6643e7e81801a7e5d809d2292529b0/OUTying_test_arange_2_23.png" data-mid="50341983" border="0"  src="https://freight.cargo.site/w/128/i/b64bcff8fb781bc7ab6a7bb50fd8336b1d6643e7e81801a7e5d809d2292529b0/OUTying_test_arange_2_23.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/d0e34cb530effb6752fd0e6270eaa3b9cd45cf8fb7600397258711b4ef3ee50f/OUTying_test_arange_11_20.png" data-mid="50341987" border="0"  src="https://freight.cargo.site/w/128/i/d0e34cb530effb6752fd0e6270eaa3b9cd45cf8fb7600397258711b4ef3ee50f/OUTying_test_arange_11_20.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/aec6885962cc62bf1f5c6d30f68a7667ee3362cb04b4f850b85106d1c7ebb7c4/OUTying_test_arange_22_90.png" data-mid="50341990" border="0"  src="https://freight.cargo.site/w/128/i/aec6885962cc62bf1f5c6d30f68a7667ee3362cb04b4f850b85106d1c7ebb7c4/OUTying_test_arange_22_90.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/30918cef7b8546d8aac4f3ae87bed8e9d7b248d443a943f303ab6fab09d2f910/OUTying_test_arange_23_99.png" data-mid="50341992" border="0"  src="https://freight.cargo.site/w/128/i/30918cef7b8546d8aac4f3ae87bed8e9d7b248d443a943f303ab6fab09d2f910/OUTying_test_arange_23_99.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/830f48b5c10e098517d75c8529a9431e4e16a73d6cc3e57199909a6b7a484383/OUTying_test_arange_41_227.png" data-mid="50342000" border="0"  src="https://freight.cargo.site/w/128/i/830f48b5c10e098517d75c8529a9431e4e16a73d6cc3e57199909a6b7a484383/OUTying_test_arange_41_227.png" /&#62;
&#60;img width="128" height="128" width_o="128" height_o="128" data-src="https://freight.cargo.site/t/original/i/8155ab7dfb32a355bb4220236d6fe77df9c690f353b9562482499789bece3fbe/OUTying_test_arange_25_118.png" data-mid="50341994" border="0"  src="https://freight.cargo.site/w/128/i/8155ab7dfb32a355bb4220236d6fe77df9c690f353b9562482499789bece3fbe/OUTying_test_arange_25_118.png" /&#62;


It’s easy to see that the second set of result is more “successful” than the first set. The first set is trying to get the gridline, structure, and color while the second set has clear gridlines and pixel blocks. This is because the second set has more “clean” data to learn from. It requires more images and time for the machine to learn if it’s a colored dataset.
However, what caught my eye is the first set of result. The intermediate generative result or the one consider “collapse” is a perfect representation of how the machine neural networks is wired, the machine generative aesthetic. In fact, I got some similar results in my last project working with other GAN models, but these glitchy color only shows in part of the image instead of the whole image. Therefore, I want to learn more about these machine glitchy results, the message encrypted in it and its implication to us: knit these glitchy images out, make them into garments/wearables, use, wear, and test them in real life context.

MAKING
︎
PRE-PRODUCTION&#38;nbsp; &#38;nbsp; &#38;nbsp;Work with algorithms to generate desired patterns:
&#38;nbsp; &#38;nbsp; &#38;nbsp; &#38;nbsp; &#38;nbsp; &#38;nbsp;︎Choose machine learning algorithm model
&#38;nbsp; &#38;nbsp; &#38;nbsp;︎Prepare dataset
&#38;nbsp; &#38;nbsp; &#38;nbsp;︎Training
&#38;nbsp; &#38;nbsp; &#38;nbsp;︎Test the result

︎
PRODUCTION &#38;nbsp;
Adjust the generative result to make it suitable for machine knitting:
︎Down-sample the result:Due to the limitation of multi-color knitting(usually 2-4 color for machine knitting), I down-sampled the pattern to 10 colors to start with(the least number of colors that still retains the original glitchy aesthetic).
&#38;nbsp; &#38;nbsp; &#38;nbsp; &#38;nbsp; &#38;nbsp;
Left(generative result) V.S Right(down-sampled image)


&#60;img width="949" height="1000" width_o="949" height_o="1000" data-src="https://freight.cargo.site/t/original/i/1d85d883a70d439df014116694682204c034c565694b676de19a4d4cc5c466f1/156264496121.jpg" data-mid="50347976" border="0"  src="https://freight.cargo.site/w/949/i/1d85d883a70d439df014116694682204c034c565694b676de19a4d4cc5c466f1/156264496121.jpg" /&#62;
&#60;img width="1920" height="1917" width_o="1920" height_o="1917" data-src="https://freight.cargo.site/t/original/i/935fbba935fd6381f10d18735a1942534bea1fada20f3d2f42fb5a66d2eeb657/yellowone.png" data-mid="50348266" border="0"  src="https://freight.cargo.site/w/1000/i/935fbba935fd6381f10d18735a1942534bea1fada20f3d2f42fb5a66d2eeb657/yellowone.png" /&#62;
&#38;nbsp; &#38;nbsp; &#38;nbsp;
&#60;img width="880" height="641" width_o="880" height_o="641" data-src="https://freight.cargo.site/t/original/i/1397cc32913b2e0b4972607cbd217663455518cbee41c6199a84d62f02b7ac27/2.png" data-mid="50348655" border="0"  src="https://freight.cargo.site/w/880/i/1397cc32913b2e0b4972607cbd217663455518cbee41c6199a84d62f02b7ac27/2.png" /&#62;
&#60;img width="1902" height="1292" width_o="1902" height_o="1292" data-src="https://freight.cargo.site/t/original/i/dd495eaf79f36acca34e2a2ccd11036ca327a41065d797102199ebd9c09d76dc/2_.png" data-mid="50348654" border="0"  src="https://freight.cargo.site/w/1000/i/dd495eaf79f36acca34e2a2ccd11036ca327a41065d797102199ebd9c09d76dc/2_.png" /&#62;
︎Replace color based on yarn choice:
Based on colors of the existing yarns, replace certain colors on the image to simulate the final product color palette, for example, replace grey with light purple.
&#38;nbsp; 


︎Make it “machine readable”:Since there are no free or even low cost program that supports 10 color pattern for the machine, I have to figure out a way to make the knitting process less painful. I wrote a simple program in Processing to make it easier for me to translate the pattern from the computer to the knitting machine:
Demo of the program:&#60;img width="1150" height="640" width_o="1150" height_o="640" data-src="https://freight.cargo.site/t/original/i/9b54bd77cec94b01ad6698fbe1a87cda507315cd4838b0172b05234c19d64aad/ProgramDemo.gif" data-mid="50353986" border="0"  src="https://freight.cargo.site/w/1000/i/9b54bd77cec94b01ad6698fbe1a87cda507315cd4838b0172b05234c19d64aad/ProgramDemo.gif" /&#62;
The program separates color for the pattern and indicates the row and needle position for each pixel, which saves me lots of time on counting and memorizing the pattern.


︎
POST-PRODUCTIONTo knit these three pieces I showed in the beginning of this document, I used three different techniques, they all are&#38;nbsp; traditional knitting techniques, but because of the glitchy pattern and number of colors I am using, I had to do it differently: 

&#60;img width="2851" height="2026" width_o="2851" height_o="2026" data-src="https://freight.cargo.site/t/original/i/9249e70662626d8251b24393291c98cb691f7bf64d431b81ac12049c3c5d9740/1_detail.jpg" data-mid="50357721" border="0"  src="https://freight.cargo.site/w/1000/i/9249e70662626d8251b24393291c98cb691f7bf64d431b81ac12049c3c5d9740/1_detail.jpg" /&#62;
&#60;img width="2358" height="1340" width_o="2358" height_o="1340" data-src="https://freight.cargo.site/t/original/i/81b439ab0a0e39c13211ea111bdd43216ca281adb6cad466145aa7ab208d64b3/2_detail.jpg" data-mid="50357722" border="0"  src="https://freight.cargo.site/w/1000/i/81b439ab0a0e39c13211ea111bdd43216ca281adb6cad466145aa7ab208d64b3/2_detail.jpg" /&#62;
&#60;img width="1487" height="956" width_o="1487" height_o="956" data-src="https://freight.cargo.site/t/original/i/915d7b5d8790228250dc017c2f8a84001d24670cb6e2b686cac0a25294589b01/3_detail.jpg" data-mid="50357723" border="0"  src="https://freight.cargo.site/w/1000/i/915d7b5d8790228250dc017c2f8a84001d24670cb6e2b686cac0a25294589b01/3_detail.jpg" /&#62;


&#60;img width="2594" height="1824" width_o="2594" height_o="1824" data-src="https://freight.cargo.site/t/original/i/6c60f5188ab06aafb4cc72f01351174731081777ece22fe94a58759b2c6987ee/1_back.jpg" data-mid="50357860" border="0"  src="https://freight.cargo.site/w/1000/i/6c60f5188ab06aafb4cc72f01351174731081777ece22fe94a58759b2c6987ee/1_back.jpg" /&#62;
&#60;img width="2736" height="1824" width_o="2736" height_o="1824" data-src="https://freight.cargo.site/t/original/i/12fdf06e70740fdd614955cc3e620d3c1c4aa4fb7436924cce6867407f670cef/2_back.jpg" data-mid="50357861" border="0"  src="https://freight.cargo.site/w/1000/i/12fdf06e70740fdd614955cc3e620d3c1c4aa4fb7436924cce6867407f670cef/2_back.jpg" /&#62;
&#60;img width="2479" height="1824" width_o="2479" height_o="1824" data-src="https://freight.cargo.site/t/original/i/158ba15b7730ed88b6674d78f76107095850bbb3121ce9f18372524bdadb143d/3_back.jpg" data-mid="50357862" border="0"  src="https://freight.cargo.site/w/1000/i/158ba15b7730ed88b6674d78f76107095850bbb3121ce9f18372524bdadb143d/3_back.jpg" /&#62;

1. Single-bed fairisle ︎ All hand manipulated, caused uneven tension surface. Long floats at the back2. Single-bed intarsia ︎ Semi-hand manipulated, a more even surface than the first one. Long floats at the back, less than the first one tho.3. Double-bed jacquard&#38;nbsp;︎ Machine knit. No floats.
Among these three technique, I prefer multicolor intarsia than the other two. Using intarsia is a good way to avoid uneven tension when hand manipulation is needed. Though double-bed jacquard solves this problem and the float problem, because too many colors were used, the pattern is elongated and you can see the back side from the front.&#38;nbsp;
The video below documents the knitting process of the second piece, using single-bed intarsia.
Technique and equipment : Single bed 9-color intarsia on both Brother standard and bulky machine 
Time: 40 hours of knitting + 10 hours of sewing 
&#38;nbsp;


&#60;img width="3819" height="4802" width_o="3819" height_o="4802" data-src="https://freight.cargo.site/t/original/i/6ced8a2210cbe918451b19d96b905a25c74ce15e96db73d5411c3c91778dec4e/1.jpg" data-mid="50357603" border="0"  src="https://freight.cargo.site/w/1000/i/6ced8a2210cbe918451b19d96b905a25c74ce15e96db73d5411c3c91778dec4e/1.jpg" /&#62;
&#60;img width="4000" height="4459" width_o="4000" height_o="4459" data-src="https://freight.cargo.site/t/original/i/6240789f034a48108528f00d3023cb07ffb17d5e73d127649611fb5db6b0209f/2.jpg" data-mid="50357604" border="0"  src="https://freight.cargo.site/w/1000/i/6240789f034a48108528f00d3023cb07ffb17d5e73d127649611fb5db6b0209f/2.jpg" /&#62;
&#60;img width="3813" height="4291" width_o="3813" height_o="4291" data-src="https://freight.cargo.site/t/original/i/05b03d86b538ac0809c17fd7719b8a2ebf59baea5d22599b79ee57f7186aab27/3.jpg" data-mid="50357605" border="0"  src="https://freight.cargo.site/w/1000/i/05b03d86b538ac0809c17fd7719b8a2ebf59baea5d22599b79ee57f7186aab27/3.jpg" /&#62;
&#60;img width="1000" height="1500" width_o="1000" height_o="1500" data-src="https://freight.cargo.site/t/original/i/2a27cf9d35c50207d0b0e041babc1173015067de053779976601ffb5a0fb1759/finger2.jpg" data-mid="50358308" border="0"  src="https://freight.cargo.site/w/1000/i/2a27cf9d35c50207d0b0e041babc1173015067de053779976601ffb5a0fb1759/finger2.jpg" /&#62;
&#60;img width="1000" height="1500" width_o="1000" height_o="1500" data-src="https://freight.cargo.site/t/original/i/8631de6aa03f0bc502929108b815326744a0fc8a8b8282ee5587c9da5d4b4fa7/finger1.jpg" data-mid="50358307" border="0"  src="https://freight.cargo.site/w/1000/i/8631de6aa03f0bc502929108b815326744a0fc8a8b8282ee5587c9da5d4b4fa7/finger1.jpg" /&#62;
&#60;img width="1000" height="1546" width_o="1000" height_o="1546" data-src="https://freight.cargo.site/t/original/i/dc8b76e1e8ca45e7db7cc417a7c72fcf7c9466f687de03dcdeb1bcfe421f6010/finger3.jpg" data-mid="50358309" border="0"  src="https://freight.cargo.site/w/1000/i/dc8b76e1e8ca45e7db7cc417a7c72fcf7c9466f687de03dcdeb1bcfe421f6010/finger3.jpg" /&#62;
&#60;img width="2109" height="1363" width_o="2109" height_o="1363" data-src="https://freight.cargo.site/t/original/i/98ec2238c2db5abe596abbf8f10caf75cfeec9a2ae4b40033ee5883178588cf6/things.jpg" data-mid="50358311" border="0" alt="knitted wearable objects" data-caption="knitted wearable objects" src="https://freight.cargo.site/w/1000/i/98ec2238c2db5abe596abbf8f10caf75cfeec9a2ae4b40033ee5883178588cf6/things.jpg" /&#62;
Internet of Knitted wearables
BRANDINGComing soon......
SPECULATION &#38;amp; REFLECTION
By applying those generative patterns to knitting, the garment become the physical container of the digital message from the machine. Through the process of pattern making, swatch production, and lifestyle integration, the knitter becomes the person who works with the image instead of on the image, translating and encoding the message into the wearable. This process creates a network of communication as well as a unique lifestyle.

Knitted pattern performance: 
Because the pattern is very dense and has a lot of features, it makes a good medium for AR pattern recognition (tested on Vuforia). This opens up possibilities for Mixed Reality communication/experience with knitwear. A connecting knitted wearable ecosystem could be created in public and private, bridging smart knitwear/knit objects with our digital world.

Knitting mechanisms: 
As I mentioned in  the research session, there is room to improve the machine mechanism to make multicolor knitting easier. Though I did some modification to the knitting technique and program, it’s not enough to make a big difference. There’s possibility to pair up with engineers and mechanics to make this improvement. In fact, CMU Textile Lab’ project, KnitOut, an on-demand knitting system is already innovating the knitting  design pipeline which makes it possible to integrate all knitting techniques even including 3D knitting.
&#38;nbsp;
Index &#38;nbsp; &#38;nbsp;Next︎
</description>
		
	</item>
		
		
	<item>
		<title>Hyper-density Add-Ons</title>
				
		<link>https://yunyingh.com/Hyper-density-Add-Ons</link>

		<pubDate>Wed, 22 May 2019 04:51:33 +0000</pubDate>

		<dc:creator>Ying Huang</dc:creator>

		<guid isPermaLink="true">https://yunyingh.com/Hyper-density-Add-Ons</guid>

		<description>Hyper-density Add-ons


2017 MDP Winter W.I.P Show


&#60;img width="1536" height="2048" width_o="1536" height_o="2048" data-src="https://freight.cargo.site/t/original/i/876df63a14f14098b6992006c9fa3ad2227724000cfc3fe694a500a7f8a2364e/IMG_0015.PNG" data-mid="42808779" border="0"  src="https://freight.cargo.site/w/1000/i/876df63a14f14098b6992006c9fa3ad2227724000cfc3fe694a500a7f8a2364e/IMG_0015.PNG" /&#62;
&#60;img width="1300" height="1804" width_o="1300" height_o="1804" data-src="https://freight.cargo.site/t/original/i/48dece5b8e5ddc0023f6d04c5b79ef8a665718a28966be3e05598bff6d1de582/Screen-Shot-2017-12-17-at-9.41.19-PM.png" data-mid="42808778" border="0"  src="https://freight.cargo.site/w/1000/i/48dece5b8e5ddc0023f6d04c5b79ef8a665718a28966be3e05598bff6d1de582/Screen-Shot-2017-12-17-at-9.41.19-PM.png" /&#62;
&#38;nbsp;

Index &#38;nbsp; &#38;nbsp;Next︎
</description>
		
	</item>
		
		
	<item>
		<title>Algorithm Jamplifying</title>
				
		<link>https://yunyingh.com/Algorithm-Jamplifying</link>

		<pubDate>Wed, 22 May 2019 04:51:27 +0000</pubDate>

		<dc:creator>Ying Huang</dc:creator>

		<guid isPermaLink="true">https://yunyingh.com/Algorithm-Jamplifying</guid>

		<description>Algorithm Jamplifying


&#60;img width="3300" height="2550" width_o="3300" height_o="2550" data-src="https://freight.cargo.site/t/original/i/5765c0bccafa109936a4d2fde7337bbf123cce48539d8bd324f85bf7241c736e/1_2.jpg" data-mid="42808687" border="0"  src="https://freight.cargo.site/w/1000/i/5765c0bccafa109936a4d2fde7337bbf123cce48539d8bd324f85bf7241c736e/1_2.jpg" /&#62;
&#60;img width="3066" height="2390" width_o="3066" height_o="2390" data-src="https://freight.cargo.site/t/original/i/e8a97c8255b8d6f26b79b50ef14cfb1bee8d4c2b36d3a1ac71b78806c4d6abe8/algorithmjamplifying-copy.jpg" data-mid="43552615" border="0"  src="https://freight.cargo.site/w/1000/i/e8a97c8255b8d6f26b79b50ef14cfb1bee8d4c2b36d3a1ac71b78806c4d6abe8/algorithmjamplifying-copy.jpg" /&#62;
&#60;img width="5472" height="3648" width_o="5472" height_o="3648" data-src="https://freight.cargo.site/t/original/i/d215bf56170fa5fec4b1d503118424058b93de9e76d546a7774de642eafd7744/_MG_8393-copy.jpg" data-mid="43552573" border="0" data-scale="100" src="https://freight.cargo.site/w/1000/i/d215bf56170fa5fec4b1d503118424058b93de9e76d546a7774de642eafd7744/_MG_8393-copy.jpg" /&#62;
&#60;img width="3648" height="5472" width_o="3648" height_o="5472" data-src="https://freight.cargo.site/t/original/i/f6421a21c9069430f4a6d443dd496bed42a2311976896fdd37eb4847bd7c62ff/_MG_8392-copy.jpg" data-mid="43552575" border="0"  src="https://freight.cargo.site/w/1000/i/f6421a21c9069430f4a6d443dd496bed42a2311976896fdd37eb4847bd7c62ff/_MG_8392-copy.jpg" /&#62;
&#60;img width="1780" height="2736" width_o="1780" height_o="2736" data-src="https://freight.cargo.site/t/original/i/7d4df3c16e1cc57cac62f9f89c3ad3ec8876d640a9561a52446401772bd37381/_MG_8452.jpg" data-mid="43552574" border="0"  src="https://freight.cargo.site/w/1000/i/7d4df3c16e1cc57cac62f9f89c3ad3ec8876d640a9561a52446401772bd37381/_MG_8452.jpg" /&#62;
&#60;img width="3648" height="4943" width_o="3648" height_o="4943" data-src="https://freight.cargo.site/t/original/i/c3085c301fff19e6df6dc25f2c799e5d1fcf689f2203c6b6f5f0589528ffa1de/_MG_8387.jpg" data-mid="43552572" border="0"  src="https://freight.cargo.site/w/1000/i/c3085c301fff19e6df6dc25f2c799e5d1fcf689f2203c6b6f5f0589528ffa1de/_MG_8387.jpg" /&#62;
&#60;img width="3648" height="5052" width_o="3648" height_o="5052" data-src="https://freight.cargo.site/t/original/i/a937a3a20df11523fca04aa277064fc86324c7c3b1442833b2cab1d4de9c417b/_MG_8378-copy.jpg" data-mid="43552571" border="0"  src="https://freight.cargo.site/w/1000/i/a937a3a20df11523fca04aa277064fc86324c7c3b1442833b2cab1d4de9c417b/_MG_8378-copy.jpg" /&#62;

&#60;img width="12176" height="1207" width_o="12176" height_o="1207" data-src="https://freight.cargo.site/t/original/i/777c6ad27345423216de9d03ed7aef0dbed0983aeaced1ad8c9e74a2ce985b69/text3.png" data-mid="43553749" border="0"  src="https://freight.cargo.site/w/1000/i/777c6ad27345423216de9d03ed7aef0dbed0983aeaced1ad8c9e74a2ce985b69/text3.png" /&#62;
Thesis project

Time: September 2018 - current
Individual Project
Exhibited at PRIMER19, New York, June 2019 / Die Digitale “Digital Overload”, Germany, Dec 2019 / NeurIPS 2019 AI Art Gallery 
Responsibility and Workflow:

- Concept development : Re-imagined and re-designed users’ relationship with AI and social media in order to give user more agency and stimulates creativity in self-expression + culture production. Developed an organic system to achieve this goal.- Research : Used Chinese social media and Chinese people as case study. Researched on topics including the current issue on AI algorithms embeded in social medias; user behaviors and tendency on social media useage; the relationship between social media and fabrication of pop culture- Coding and prototyping : Designed and prototyped a website platform with 4 functional generative AI algorithm on it for users to generate social media contents and share them directly to social platforms.- Workshop :&#38;nbsp; Gave workshop on instructions of the website platform and facilitated the styling process, reflect on the insights and modify the platform accordingly.- Post-production : Generated videos, collage, print materials, documentation,and built the installation to present the project.&#38;nbsp;

INTRO&#38;nbsp;
Algorithm Jamplifying imagines and develops new behavior and aesthetics evolving in Chinese pop culture. Users and cultural producers adopt A.I. algorithms as a new way to generate memes, videos, and aesthetics. By facilitating this collaboration with A.I., users are able to exert control over their presence on social platforms and create content that amplifies their desires, values, and creativity.

The work consists of 4 parts: the website platform for the public to generate jamplified contents, the direct output (social media meme, videos, aesthetic people generate) from the website platform, social media feed showing the way people consuming these AI content, and glossary explaining the spirit of the project.&#38;nbsp;
 
&#60;img width="13189" height="2052" width_o="13189" height_o="2052" data-src="https://freight.cargo.site/t/original/i/226f52fa89bb3b73eef2f06b662a506e77b2e5c9c6837132ddf544445ee29e64/Glossary_Group-3.png" data-mid="43553746" border="0"  src="https://freight.cargo.site/w/1000/i/226f52fa89bb3b73eef2f06b662a506e77b2e5c9c6837132ddf544445ee29e64/Glossary_Group-3.png" /&#62;
BACKGROUND
AI is
living in every corner of our lives, it’s embedded in CCTV, mobile device,
social platforms, and more. Its ability for recognition, recommendation, prediction,
and content generation is constituting and fabricating a hyperreality for
Chinese people, which manifested through Chinese pop culture. We are living in
a bubble that AI selected for us, our imagination is trapped. AI has pushed
China to its next level of Ultra-Unreal, the reality of overloaded distorted
information, the enormous growth of the Chinese economy, warp speed
modernization, and political oppression.

Algorithm Jamplifying is a strategy for users to
escape this flood and reclaim control over social platforms.


&#60;img width="12779" height="1248" width_o="12779" height_o="1248" data-src="https://freight.cargo.site/t/original/i/068d8a5ba3ef916929a9df1d01ba44edc80e02e72b70eb06b66b549cc642a1ca/text_1.png" data-mid="43553747" border="0"  src="https://freight.cargo.site/w/1000/i/068d8a5ba3ef916929a9df1d01ba44edc80e02e72b70eb06b66b549cc642a1ca/text_1.png" /&#62;&#60;img width="11856" height="1441" width_o="11856" height_o="1441" data-src="https://freight.cargo.site/t/original/i/0c853d1da3cd0bb440d9856158800e924bf4513aa75295b41c82e775c1e360e8/text2.png" data-mid="43553748" border="0"  src="https://freight.cargo.site/w/1000/i/0c853d1da3cd0bb440d9856158800e924bf4513aa75295b41c82e775c1e360e8/text2.png" /&#62;
 RESEARCH
My research questions sought to consider how Chinese social media and AI algorithms would change both functionally and aesthetically to accomodate for a more authentic self-expression platform.︎How is the current AI algorithms embeded in Chinese social medias influencing the formation and expression of modern Chinese pop culture?&#38;nbsp;︎How can AI algorithms be more transparent and viewed as a creative inspiration rather than a algorithmic restriction to imgination?To begin, I did lots of visual and data investigation on the current Chinese pop culture and social media platforms including Tiktok,Weibo, Wechat. 
I used web scraping technique and myself to collect videos, text memes from Tiktok and Weibo as a way to prepare for my data visualization analysis and initial observation on its algorithmic mechanisim. My observation mainly focused on the platform’s recommendation systems, ranking systems, tagging strategy, and catagorizing strategy. 
For my video data visualization analysis, I used video collage, played with scale and number to gather insights from my data using large scale projection mapping.&#38;nbsp;As for Weibo, a text-focused social platform, I used algorithm to calculate and analyze those text memes, then used AI algorithm to re-generate these memes to test my observation.

&#60;img width="6000" height="3368" width_o="6000" height_o="3368" data-src="https://freight.cargo.site/t/original/i/94583a638c7a7fd7ef763fbbe35dbdef93a81950add1d69b05ce88e33fbae0e8/projection.jpg" data-mid="46379892" border="0"  src="https://freight.cargo.site/w/1000/i/94583a638c7a7fd7ef763fbbe35dbdef93a81950add1d69b05ce88e33fbae0e8/projection.jpg" /&#62;
&#60;img width="3176" height="1938" width_o="3176" height_o="1938" data-src="https://freight.cargo.site/t/original/i/78e5030b70e91b1aeb797a69cc7b8325c90f80fd72ec718af3bc3f63a3763b96/Screen-Shot-2019-07-01-at-5.23.12-PM.png" data-mid="45794859" border="0" alt="Screenshot of the video collage projection" data-caption="Screenshot of the video collage projection" src="https://freight.cargo.site/w/1000/i/78e5030b70e91b1aeb797a69cc7b8325c90f80fd72ec718af3bc3f63a3763b96/Screen-Shot-2019-07-01-at-5.23.12-PM.png" /&#62;
&#60;img width="3228" height="2136" width_o="3228" height_o="2136" data-src="https://freight.cargo.site/t/original/i/3a75b76d8665f4c305e6e95eafeddeecd1c6622bd9616ff9bd135783ba860e6b/Screen-Shot-2019-07-01-at-5.23.20-PM.png" data-mid="45794860" border="0"  src="https://freight.cargo.site/w/1000/i/3a75b76d8665f4c305e6e95eafeddeecd1c6622bd9616ff9bd135783ba860e6b/Screen-Shot-2019-07-01-at-5.23.20-PM.png" /&#62;
Initial Research presentation
&#60;img width="5184" height="3456" width_o="5184" height_o="3456" data-src="https://freight.cargo.site/t/original/i/02569c5e57821937400d42962b3e1dde2ee2700c9f93b724c6feb259b75ed170/IMG_9799.jpg" data-mid="45794982" border="0" data-scale="98" src="https://freight.cargo.site/w/1000/i/02569c5e57821937400d42962b3e1dde2ee2700c9f93b724c6feb259b75ed170/IMG_9799.jpg" /&#62;

DESIGN / PROCESS

︎DIAGRAM&#38;nbsp;
Jamplified content generator system diagram:
&#60;img width="1920" height="1080" width_o="1920" height_o="1080" data-src="https://freight.cargo.site/t/original/i/6d3b394c1c78fbf30cb6b108fc3181989dc01455df9282c9cb1c482c07cdfc72/diagram.gif" data-mid="42808665" border="0" data-scale="91" src="https://freight.cargo.site/w/1000/i/6d3b394c1c78fbf30cb6b108fc3181989dc01455df9282c9cb1c482c07cdfc72/diagram.gif" /&#62;
The diagram illustrates conclusion from my research and the whole design system concept:&#38;nbsp; 
The first row shows the feedback loop between AI algorithms and user behavior and content. AI algorithms read user profile, record user behavior, tag user uploaded contents and use these data to create filter bubbles, give personalized recommendations, and guiding speicific trends. 
The row below explains my design – Use AI to interfere with AI. 
It achieves this by initiating a feedback loop that starts by pulling content uploaded by users from exsiting social platforms, populating a database for a new neural network algorithm with that content, generating new content based on that algorithm, and reposting this content on social platforms. After this content is shared on social platforms, it becomes: 1) creative noise for the original AI algorithm to function differently, update itself, creating new filter bubbles; 2) the next round of source material for the AI; 3) creative inspiration for users to produce new content. In effect, the content becomes “jamplified”: Making, sharing, and consuming in this way amplifies the “noise” being injected on social platform and users’ agency.
︎WEBSITE PLATFORM&#38;nbsp;


https://2w1djam.com
The website guide user through the spirit of the concept, and the usgae of the function, enabling users to generate social media content with AI and share them directly to their Weibo, Tiktok, and Wechat.


&#60;img width="3352" height="1882" width_o="3352" height_o="1882" data-src="https://freight.cargo.site/t/original/i/b52585a744ed091a4dba09ebfe104118d2afab92cd110bf76a14d2a463439966/memes.jpg" data-mid="43553108" border="0"  src="https://freight.cargo.site/w/1000/i/b52585a744ed091a4dba09ebfe104118d2afab92cd110bf76a14d2a463439966/memes.jpg" /&#62;
&#60;img width="648" height="1152" width_o="648" height_o="1152" data-src="https://freight.cargo.site/t/original/i/c108b8261f58b38c46f4855113ad265ebb032de0fc9ee799cd47d705a0e88310/ezgif.com-gif-maker.gif" data-mid="43553106" border="0"  src="https://freight.cargo.site/w/648/i/c108b8261f58b38c46f4855113ad265ebb032de0fc9ee799cd47d705a0e88310/ezgif.com-gif-maker.gif" /&#62;
&#60;img width="648" height="1152" width_o="648" height_o="1152" data-src="https://freight.cargo.site/t/original/i/fc59ca02d7310b64e64d964b81e1b2cd70510e82a35c85740ac6b91a239c7d76/tiktok_out.gif" data-mid="43553107" border="0"  src="https://freight.cargo.site/w/648/i/fc59ca02d7310b64e64d964b81e1b2cd70510e82a35c85740ac6b91a239c7d76/tiktok_out.gif" /&#62;

︎SOCIAL MEDIA FEED
Showing the evolving expression of Chinese pop culture and the new aesthetic of social media.

&#60;img width="13181" height="2022" width_o="13181" height_o="2022" data-src="https://freight.cargo.site/t/original/i/7f2387a601cffeac1063205e4b9313d6e43a12e0717b2c941a253e12e401d5f0/Glossary_Group-1-copy-4.png" data-mid="43553745" border="0"  src="https://freight.cargo.site/w/1000/i/7f2387a601cffeac1063205e4b9313d6e43a12e0717b2c941a253e12e401d5f0/Glossary_Group-1-copy-4.png" /&#62;

Index &#38;nbsp; &#38;nbsp;Next︎
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		<title>Proxy Limbs</title>
				
		<link>https://yunyingh.com/Proxy-Limbs</link>

		<pubDate>Wed, 22 May 2019 04:51:32 +0000</pubDate>

		<dc:creator>Ying Huang</dc:creator>

		<guid isPermaLink="true">https://yunyingh.com/Proxy-Limbs</guid>

		<description>PROXY LIMBS


An Exploration on Trackability in Virtual RealityUnity / HTC VIVE / KINECT / LEAP MOTION / VIVE TRAKCERSGroup Project with Sohee Woo and Tianlu Tang
Time: 4 weeks&#38;nbsp;Role: concept building / 3D Modeling / codingResponsibility and Workflow:
- Concept development : Explored the effects of multi platform tracking experience on both the performance of the VR and the user(actor).- Research : Researched on the current available VR tracking devices and its tracking mechanisms.- 3D Modelling : Created 3D models and textures in Maya and Unity.
- Coding and prototyping : Accomplished the whole interaction part of the project on Unity(C#).&#38;nbsp;

INTROInspired by the variety of virtual reality systems available today, HyperTracking serves as an exploration and comparison on the effects of the single platform vs. multi platform tracking experience. With the availability of cross-platform tracking it opens up the opportunity to combine specialized systems into one VR experience. ( For example, detailed finger tracking with leap motion in tandem with the full body rigged tracking of the Xbox Kinect.) When these different technologies exist in the same space, it creates a distorted and displaced sense of multiple proxy limbs, in turns opens up the opportunity for catered specialized skill performance and interaction, and a more autonomous VR experience.


&#60;img width="1296" height="1200" width_o="1296" height_o="1200" data-src="https://freight.cargo.site/t/original/i/934285504c5e12d17e6e2c088d65b2a93a54f7c9b81c2dfb6ac7582d5643763c/Screen-Shot-2018-02-16-at-4.08.56-PM.png" data-mid="42808762" border="0"  src="https://freight.cargo.site/w/1000/i/934285504c5e12d17e6e2c088d65b2a93a54f7c9b81c2dfb6ac7582d5643763c/Screen-Shot-2018-02-16-at-4.08.56-PM.png" /&#62;
&#60;img width="1916" height="1772" width_o="1916" height_o="1772" data-src="https://freight.cargo.site/t/original/i/be4694f5b585e0e57056d22af47d14aead0cbe9a21236942a628a1fd1987bafb/Screen-Shot-2018-02-16-at-3.53.15-PM.png" data-mid="42808759" border="0"  src="https://freight.cargo.site/w/1000/i/be4694f5b585e0e57056d22af47d14aead0cbe9a21236942a628a1fd1987bafb/Screen-Shot-2018-02-16-at-3.53.15-PM.png" /&#62;


RESEARCH
Different platforms/devices allow for different tracking fidelity.&#38;nbsp; These platforms/devices include: HTC Vive Lighthouse, Vive Headset, Vive controller, Vive extra tracker, Leap Motion, Kinect xbox360.&#60;img width="4000" height="6000" width_o="4000" height_o="6000" data-src="https://freight.cargo.site/t/original/i/e9d51f913da71804ff83f32f4430019e75a857a38e66ca3925294c5a689f8aa8/hypertracking.jpg" data-mid="43554643" border="0" data-scale="78" src="https://freight.cargo.site/w/1000/i/e9d51f913da71804ff83f32f4430019e75a857a38e66ca3925294c5a689f8aa8/hypertracking.jpg" /&#62;
When these devices/platforms exist in the same digital space: Kinect(full body tracking), Vive headset(head tracking), Vive controller(hand tracking), extra trackers(feet tracking), leap motion(detailed finger tracking), 
How they would affect each other’s tracking performance and thus affect the performance of the user? 
How would it change the perception of self in the digital experience?
To begin,&#38;nbsp; we used the method rapid prototyping and thinking through making to exlore the implication of tracking and resolution in VR under different contexts.
PROTOTYPESFirst Prototyping

In this prototype, we explored the the influence of the number of lighthouses on trackabilty in VR. We used hands in different fidelity(from skeleton, polygon, to detailed hand) as a metaphor to represent different level of rendering and trackability&#38;nbsp; in VR. We also used light cones in the scene to represent VR outside-in laser lighthouses(the VR lighthouse uses non-visible light emitters to track objects).&#38;nbsp;

&#60;img width="518" height="474" width_o="518" height_o="474" data-src="https://freight.cargo.site/t/original/i/30c1222e7f40acb8f4866fe0cafeed9954e95df2ab3142e2a45c919505a695ac/Screen-Shot-2018-04-15-at-2.08.06-AM.png" data-mid="42808765" border="0" alt="Trackability lv.1 - Skeleton- not well rendered" data-caption="Trackability lv.1 - Skeleton- not well rendered" src="https://freight.cargo.site/w/518/i/30c1222e7f40acb8f4866fe0cafeed9954e95df2ab3142e2a45c919505a695ac/Screen-Shot-2018-04-15-at-2.08.06-AM.png" /&#62;
&#60;img width="404" height="388" width_o="404" height_o="388" data-src="https://freight.cargo.site/t/original/i/72536dc5935d4b9f8ec48172f1c2eb5d17e2412e1196583db4469033d70dd9d4/Screen-Shot-2018-04-15-at-2.08.26-AM.png" data-mid="42808767" border="0" alt="Trackability lv2. -  polygon - intermediate rendered" data-caption="Trackability lv2. -  polygon - intermediate rendered" src="https://freight.cargo.site/w/404/i/72536dc5935d4b9f8ec48172f1c2eb5d17e2412e1196583db4469033d70dd9d4/Screen-Shot-2018-04-15-at-2.08.26-AM.png" /&#62;
&#60;img width="414" height="334" width_o="414" height_o="334" data-src="https://freight.cargo.site/t/original/i/9a274f08db04314c684e246b5878e89228836134979840837864c093363768d1/Screen-Shot-2018-04-15-at-2.08.17-AM.png" data-mid="42808766" border="0" alt="Trackability lv3. - skin - well rendered" data-caption="Trackability lv3. - skin - well rendered" src="https://freight.cargo.site/w/414/i/9a274f08db04314c684e246b5878e89228836134979840837864c093363768d1/Screen-Shot-2018-04-15-at-2.08.17-AM.png" /&#62;
Second Prototyping

In this prototype, we explored how different devices with same tracking technique can affect each other both in ways of functionality and being able to tracked in VR. The video shows an example of the living room, how the remote control, in the environment of everything is being tracked in VR is being affected by this outside-in tracking technology(both remote control and lighthouse uses the the same tracking technique, emitters, to trigger objects).
In the video, it illustrates using remote control in real life and VR online at the same time could cause glitch in either technique/devices.&#38;nbsp;

Third Prototype

In this exploration, similar as the first one, we used resolution to show trackability in VR, but this time, we set the context in a much larger urban scenes to add more complex interaction and variables to explore with. Referencing the Concentric zone model,we structured the urban space into rings, simulating a city structure accomodating VR technology. The outer ring is the least tracking area(least number of lighthouses) while the inner is the highest(most number of lighthouses). We also set some random agents to observe their behaviors and resolution changes while going in and out of three rings. 
In this scenario, the different levels of resolution are: wireframe(outer ring), flat color(second ring), color with materials(third ring), color with materials plus wireframe (over-rendered).
&#60;img width="1721" height="802" width_o="1721" height_o="802" data-src="https://freight.cargo.site/t/original/i/d06d7ff2cb0ea3addaa51fc8a244a857935a8d684b0104748106b48e1f8d713c/Capture13.jpg" data-mid="42808769" border="0" alt="Agents with different rendering / trackability levels" data-caption="Agents with different rendering / trackability levels" src="https://freight.cargo.site/w/1000/i/d06d7ff2cb0ea3addaa51fc8a244a857935a8d684b0104748106b48e1f8d713c/Capture13.jpg" /&#62;
&#60;img width="1810" height="889" width_o="1810" height_o="889" data-src="https://freight.cargo.site/t/original/i/1fb9c3cdb78131093e3ed193c4230cafb601d3d96f03135a8f475c6100bd1ae0/Capture7.PNG" data-mid="42808770" border="0" alt="The concentric zone urban space" data-caption="The concentric zone urban space" src="https://freight.cargo.site/w/1000/i/1fb9c3cdb78131093e3ed193c4230cafb601d3d96f03135a8f475c6100bd1ae0/Capture7.PNG" /&#62;

In this scenario, user is able to experience different trackability levels in an urban space and interacting with agents which are represented as citizens. Referencing the Concentric zone model, this is a starting point for studying the influence of trackability on social distribution of a space in VR.
Final Prototype

In this final production, we combined our insights from previous explorations on VR tracking. Tracking in virtual reality utilizes sensors to deduct one’s position in space leaving the impressions of real time movement in a virtual environment. Using the HTC Vive as a case study, the more trackers available the more 360 degree tracking coverage you have, resulting in a higher resolution of one’s virtual self. HyperTracking, creates a visual experience illustrating what it means to be more actualized via more tracking in the VR world. In comparison, cross-platform tracking creates more specialized possibilities as well as a warped sense of self. HyperTracking utilizes HTC vive controllers and trackers, Xbox Kinect, and Leap Motion to deliver different versions of one’s self in the same space. You are expected to relearn and redefine how to move and function when one’s body parts are no longer the direct reflection of reality. 


&#60;img width="1678" height="825" width_o="1678" height_o="825" data-src="https://freight.cargo.site/t/original/i/ea049c71879e9c8e5756e7dc2003ce1b8702df0d0634e1c90fb2289d79263cb2/proxylimbs1.jpg" data-mid="43846311" border="0"  src="https://freight.cargo.site/w/1000/i/ea049c71879e9c8e5756e7dc2003ce1b8702df0d0634e1c90fb2289d79263cb2/proxylimbs1.jpg" /&#62;
&#60;img width="1674" height="826" width_o="1674" height_o="826" data-src="https://freight.cargo.site/t/original/i/1ac393b390b81c9b579a49a6abfa43c4d7c6a4f82c532bb971a10fe9efda03fc/proxylimbs2.jpg" data-mid="43846312" border="0"  src="https://freight.cargo.site/w/1000/i/1ac393b390b81c9b579a49a6abfa43c4d7c6a4f82c532bb971a10fe9efda03fc/proxylimbs2.jpg" /&#62;A warped sense of self —&#38;gt; the “hyper tracking” space and scaling, warping of proxy limbs enables a confusing removed sense of self. This creates a unique experience where you can only achieve in the VR space, it also brings opportunity for new user-digital interaction experience.


&#60;img width="4000" height="6000" width_o="4000" height_o="6000" data-src="https://freight.cargo.site/t/original/i/3da687595facb337226a5835334ecc4467626001d182b6b70289bb4090dd0073/DSC04402.JPG" data-mid="43554792" border="0"  src="https://freight.cargo.site/w/1000/i/3da687595facb337226a5835334ecc4467626001d182b6b70289bb4090dd0073/DSC04402.JPG" /&#62;
&#60;img width="4000" height="5507" width_o="4000" height_o="5507" data-src="https://freight.cargo.site/t/original/i/2d1c53a864e915e65a5afa1d8da60da74274a3403e73e7705f5a999254a76bfb/DSC04433.JPG" data-mid="43554795" border="0"  src="https://freight.cargo.site/w/1000/i/2d1c53a864e915e65a5afa1d8da60da74274a3403e73e7705f5a999254a76bfb/DSC04433.JPG" /&#62;
&#60;img width="4000" height="6000" width_o="4000" height_o="6000" data-src="https://freight.cargo.site/t/original/i/6e8d1f203d229d43c92646dbec793c145be4ef7849c728921fc8caa5e37492d4/DSC04429.JPG" data-mid="43554794" border="0"  src="https://freight.cargo.site/w/1000/i/6e8d1f203d229d43c92646dbec793c145be4ef7849c728921fc8caa5e37492d4/DSC04429.JPG" /&#62;
&#60;img width="4000" height="6000" width_o="4000" height_o="6000" data-src="https://freight.cargo.site/t/original/i/d3415580e54869d93a3ff63e392c1003501dc05a4ea507a1f494ef95bfea9724/DSC04412.JPG" data-mid="43554793" border="0"  src="https://freight.cargo.site/w/1000/i/d3415580e54869d93a3ff63e392c1003501dc05a4ea507a1f494ef95bfea9724/DSC04412.JPG" /&#62;


Reflection and further questions:
Rapid prototyping and thinking through making are an effective way to quickly and broadly learn about and explore possibilities of a technology or product. Each prototype has potential to be a brief of a research project. In this exploration, I worked with 3 different platforms in 1 digital environment, there are 7 different possibilities of using them together or seperatly to create varies effects and interaction. In this case, how, in specific, would different combination of platforms enhance or decrease user’s the digital experience ? What new forms of interaction can be generated?

Index &#38;nbsp; &#38;nbsp;Next︎
</description>
		
	</item>
		
		
	<item>
		<title>Lenovo New Year 2017</title>
				
		<link>https://yunyingh.com/Lenovo-New-Year-2017</link>

		<pubDate>Thu, 23 May 2019 23:32:40 +0000</pubDate>

		<dc:creator>Ying Huang</dc:creator>

		<guid isPermaLink="true">https://yunyingh.com/Lenovo-New-Year-2017</guid>

		<description>&#38;nbsp;
Lenovo New Year 2017


&#60;img width="900" height="1940" width_o="900" height_o="1940" data-src="https://freight.cargo.site/t/original/i/821c97d9cf4a82d17dc26695ecc24d1a3b5f0332e1253d42bb2514deccdba017/0114af58cf6b01a801219c7721a6ca.jpg2o-1.jpg" data-mid="42961279" border="0"  src="https://freight.cargo.site/w/900/i/821c97d9cf4a82d17dc26695ecc24d1a3b5f0332e1253d42bb2514deccdba017/0114af58cf6b01a801219c7721a6ca.jpg2o-1.jpg" /&#62;

&#60;img width="900" height="7968" width_o="900" height_o="7968" data-src="https://freight.cargo.site/t/original/i/846d0cad927dcb3a62cdc672203204630680973247382d48665b3ceeb6b72d2f/01baed58cf6b56a801219c776c1672.jpg1280w_1l_0o_100sh.jpg" data-mid="42961781" border="0"  src="https://freight.cargo.site/w/900/i/846d0cad927dcb3a62cdc672203204630680973247382d48665b3ceeb6b72d2f/01baed58cf6b56a801219c776c1672.jpg1280w_1l_0o_100sh.jpg" /&#62;Index &#38;nbsp; &#38;nbsp;Next︎
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		<title>Lenovo Christmas 2016</title>
				
		<link>https://yunyingh.com/Lenovo-Christmas-2016</link>

		<pubDate>Wed, 29 May 2019 04:57:21 +0000</pubDate>

		<dc:creator>Ying Huang</dc:creator>

		<guid isPermaLink="true">https://yunyingh.com/Lenovo-Christmas-2016</guid>

		<description>&#38;nbsp;
Lenovo Christmas 2016


Index &#38;nbsp; &#38;nbsp;Next︎
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