Manufacturing Companies for FOND 1W AC85-265V stainless steel LED Underground lights Factory in Guyana

Short Description:

Details Light Source: LED Item Type: LED Underground Lights Place of Origin: Zhejiang, China  Brand Name: FOND Input Voltage( V ): AC85-265 Lamp Power( W ): 1 Lamp Body Material: Stainless steel Color: R/ Y / B/ G /W / RGB Working Temperature ( ℃ ): - 30 – 50 Color Temperature ( CCT): RGB, white Lamp Luminous Flux ( lm ) 100 Lamp Luminous Efficiency( lm/w ) 100 CRI( Ra> ): 80 Beam Angle( ° ): 45 IP Rating: IP65 Certifi...


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Manufacturing Companies for FOND 1W AC85-265V stainless steel LED Underground lights Factory in Guyana Detail:

Details

Light Source:

LED

Item Type:

LED Underground Lights

Place of Origin:

Zhejiang, China 

Brand Name:

FOND

Input Voltage( V ):

AC85-265

Lamp Power( W ):

1

Lamp Body Material:

Stainless steel

Color:

R/ Y / B/ G /W / RGB

Working Temperature

( ℃ ):

- 30 – 50

Color Temperature

( CCT):

RGB, white

Lamp Luminous Flux

( lm )

100 Lamp Luminous Efficiency( lm/w ) 100

CRI( Ra> ):

80

Beam Angle( ° ):

45

IP Rating:

IP65

Certification:

CE, RoHS

 

 

Packaging & Delivery

Packaging Details: on the customer request.

Delivery Detail: 15 days receipt of the deposit.

 

Product Parameter

 

Shell Material: Stainless steel & Die-casting Aluminum

 

Model No.: FN-UG-S301

 

Input Voltage: AC85-265V 

 

Power Consumption:1 x 1W

 

Protection grade: IP65

 

Color: Cool White/Warm White / Blue /RGB Mixed

 

Luminous Flux: 120lm/w

 

Color Temperature(CCT): 2700K-6500K

 

Place of Origin: Ningbo, China 

 

CRI(Rad>) ≥80

 

Size:    D120mm K94mm H80mm   

 

Feature And Application

 

Environmental-friendly and energy saving

Easy installation

Long lifespan and stable performance

Waterproof and dust proof

Perfect for outdoor lighting such as landscape, garden, swimming pool, etc

Because of factors such as display pixel and light, there may be a little color different.

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Why are you choicing us?

 

Ningbo Fond-lighting Company Ltd. has advanced production equipment and testing instruments.

Right now, the new LED flood light and Led underwater light have successfully won a place in the market.

And, the products of our company has been approved by GS, CE, EMC, RoHS, REACH, PAHS, SAA, UL, DLC which is

surperior to the peer company. “High starting point, high quality, high pursuit” is our purpose.

The principle of our company is  providing  users with good quality products and prompt after-sales service in all

directions.

 FAQ

1. How about our company?

We have been specializing in LED flood light, LED high bay light, LED street light, LED underwater light, LED underground light for 10 years.

2. Do you accept OEM order?

Yes, OEM for our LED lights are acceptable.

3. How long is the delivery time?

It ‘s about 15-20 days.

4. What’s your payment terms?

30% TT IN ADVANCE,70% BEFORE SHIPPMENT, OR L/C.


Product detail pictures:

Manufacturing Companies for FOND 1W  AC85-265V stainless steel LED Underground lights Factory in Guyana detail pictures

Manufacturing Companies for FOND 1W  AC85-265V stainless steel LED Underground lights Factory in Guyana detail pictures


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    Google Tech Talk
    11/13/2012

    Presented by Yoshua Bengio

    ABSTRACT

    Yoshua Bengio will give an introduction to the area of Deep Learning, to which he has been one of the leading contributors. It is aimed at learning representations of data, at multiple levels of abstraction. Current machine learning algorithms are highly dependent on feature engineering (manual design of the representation fed as input to a learner), and it would be of high practical value to design algorithms that can do good feature learning. The ideal features are disentangling the unknown underlying factors that generated the data. It has been shown both through theoretical arguments and empirical studies that deep architectures can generalize better than too shallow ones. Since a 2006 breakthrough, a variety of learning algorithms have been proposed for deep learning and feature learning, mostly based on unsupervised learning of representations, often by stacking single-level learning algorithms. Several of these algorithms are based on probabilistic models but interesting challenges arise to handle the intractability of the likelihood itself, and alternatives to maximum likelihoods have been successfully explored, including criteria based on purely geometric intutions about manifolds and the concentration of probability mass that characterize many real-world learning tasks. Representation-learning algorithms are being applied to many tasks in computer vision, natural language processing, speech recognition and computational advertisement, and have won several international machine learning competitions, in particular thanks to their ability for transfer learning, i.e., to generalize to new settings and classes.

    Speaker Info

    PhD in CS from from McGill University, Canada, 1991, in the areas of HMMs, recurrent and convolutional neural networks, and speech recognition. Post-doc 1991-1992 at MIT with Michael Jordan. Post-doc 1992-1993 at Bell Labs with Larry Jackel, Yann LeCun, Vladimir Vapnik. Professor at U. Montreal (CS & operations research) since 1993. Canada Research Chair in Statistical Learning Algorithms since 2000. Fellow of the Canadian Institute of Advanced Research since 2005. NSERC industrial chair since 2006. Co-organizer of the Learning Workshop since 1998. NIPS Program Chair in 2008, NIPS General Chair in 2009. Urgel-Archambault Prize in 2009. Fellow of CIRANO. Current or previous associate/action editor for Journal of Machine Learning Research, IEEE Transactions on Neural Networks, Foundations and Trends in Machine Learning, Computational Intelligence, Machine Learning. Author of 2 books and over 200 scientific papers, with over 9000 Google Scholar citations in 2011.


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