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HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histog…
HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms with Concept Drift
SwissLink: High-Precision, Context-... by eXascale Infolab 112 views
Presented at ICDM 2017
Overall. Highlight some words (lots of monotonous text)
Just from looking at the photo, what location is this? Can’t go there but I know the places I’ve been to.
Check from these which is the most similar.
Analogy: full histogram = going there (potentially high effort, costly)
Sketch = looking at photo to judge similarity to known locations
Talk about concept drift
You may be familiar with common sketching techniques such as the very simple bloom filter or the count min sketch; these are not similarity-preserving
V: classical histogram is a vector of ever-growing cardinality
W_t are inversely proportional to the age
K is significantly smaller than the actual cardinality ℰ
Refer to paper for the proofs of these properties
Keep both sketch S and its corresponding hash values A
input: V, sketch length K, random variables r, c, 𝛽
output: S with hash values A
for 𝑗=1…𝐾:
Compute 𝑦 𝑖,𝑗
Compute 𝑎 𝑖,𝑗 using 𝑦 𝑖,𝑗
Set sketch element 𝑆 𝑗 = 𝑎𝑟𝑔𝑚𝑖𝑛 𝑖∈ℇ 𝑎 𝑖,𝑗
Set hash value 𝐴 𝑗 = 𝑚𝑖𝑛 𝑖∈ℇ 𝑎 𝑖,𝑗
Sum-to-one normalization ~ uniform scaling => can scale only on A and still be correct
Upon arrival of 𝑥 𝑡+1
Weights of existing histogram elements are adjusted by a factor of 𝑒 −𝜆 by scaling 𝐴.
Add incoming element 𝑖′ with weight 1 to scaled histogram.
Recompute 𝑎 𝑖′,𝑗 ; 𝑆 𝑗 𝑡+1 =𝑖′ if 𝑎 𝑖′,𝑗 < 𝐴 𝑗 𝑡 ∙ 𝑒 𝜆 , else 𝑆 𝑗 𝑡 𝐴 𝑗 𝑡+1 = 𝑎 𝑖′,𝑗 if 𝑎 𝑖′,𝑗 < 𝐴 𝑗 𝑡 ∙ 𝑒 𝜆 , else 𝐴 𝑗 𝑡 ∙ 𝑒 𝜆
Approximation = overall accuracy
Adaptation to concept drift = accuracy recovery speed after concept drift
Histogram-Classical adapts very slowly
Histogram-Forgetting is fast to adapt
HistoSketch adapts just as quickly as Histogram-Forgetting to concept drift
Sketch length K has no impact on adaptation speed, but has a positive impact on the classification accuracy: larger K  longer sketch  more accurate as they better approximate the original histogram
Although there is no big difference beyond K=500 which is almost equal to Histogram-Forgetting
Trade-off between concept drift adaptation speed and accuracy: a high weight decay factor means quicker recovery, as outdated data is quickly forgotten, but overall lower accuracy, as less information from former histograms is used
We observe the same trade-off in Histogram-LatestK, with its adaptation being slower than 0.05 and faster than 0.02, but also its accuracy being higher than 0.05 and lower than 0.02
Advantages of HistoSketch: HistoSketch can balance adaptation speed and accuracy, and HistoSketch is much faster for similarity computation (relies only on Hamming distance)
Reminder: what is POI
Different places typically have different temporal visiting patterns
Fine-grained patterns: user+time instead of only time gives more accuracy
POI abrupt change: e.g. change of type of POI – clothing store to art gallery
POI gradual change: e.g. small change in POI – new menu items at a restaurant
Split into 80% train and 20% unlabeled test
Histogram-coarse: (i.e. visitor count per time slot, no information on user)
POISketch = HistoSketch with decay factor 0
Histogram-Coarse: worst accuracy (to be expected)
Histogram-Fine-Forgetting: highest accuracy
HistoSketch outperforms POISketch due to gradual forgetting weights
HistoSketch: small loss in accuracy against Histogram-Fine-Forgetting
HistoSketch presents a speedup against Forgetting of about 7500x since the hamming distance can be computed much more efficiently than normalized min-max similarity between full histograms
HistoSketch also takes much less memory to maintain
Longer sketches = slightly higher processing time, but still much less than other methods
Can handle real-world scenarios: Foursquare: peaks at 7 million check-ins per day which is 81 check-ins per second
Method is also parallelizable as sketches for different POIs can be independently maintained
TODO: Add normalization
Maybe remove this slide
Accuracy increases over time with more information
POISketch approximates Classical, HistoSketch approximates Forgetting => higher accuracy
Larger improvement with the presence of abrupt drift: HistoSketch is more accurate at handling concept drift than Classical by approximating Forgetting
No sudden drop in accuracy as POIs don’t change their type simultaneously
1. HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms with Concept Drift Dingqi Yang*, Bin Li†, Laura Rettig*, Philippe Cudré-Mauroux* *eXascale Infolab, University of Fribourg, Switzerland †School of Computer Science, Fudan University, Shanghai, China 1
2. HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms with Concept Drift 2 What kind of location is this? Places I’ve been: Bar University Museum Supermarket 0.7 0.6 0.14 0.21 0.41 0.63 0.64 0.65 0.21 0.86 0.24 0.82 0.64 0.65 0.21 0.86 0.24 0.82 0.7 0.6 0.14 0.21 0.41 0.63 Compute similarity ?
3. Motivation • Histogram similarity: foundation for many machine learning tasks • Cardinality of histograms over data streams continuously increases • Similarity-preserving data sketches • Compact, fixed size • Preserve similarity under certain measure • Are incrementally updateable • Concept drift: distribution of a histogram changes over time • If taken into account can improve accuracy of histogram-based similarity techniques • Typical method: gradual forgetting 3
4. Background Given a data stream of incoming elements xt, with a weight wt we compute a histogram V such that Vi is the weighted cumulative count of the element i. 4 xtxt-1... Streaming histogram elements xt with wt Corresponding histogram V xt-2
5. Problem Formulation • Create and maintain the similarity-preserving sketch S for the full streaming histogram V such that • each sketch has a fixed size K (K≪ |ℰ|); • the collision probability between two sketches Sa and Sb is the normalized similarity between the histograms Va and Vb  the Hamming distance between Sa and Sb approximates SIMNMM(Va, Vb); • the sketch S(t+1) can be efficiently computed from the incoming histograms element xt+1, S(t), and a weight decay factor λ. 5 xtxt-1... New element xt+1 received Incremental updating xt+1 S(t+1) xt+1S(t) λ xt-2
6. HistoSketch • Based on the idea of consistent weighted sampling • Generate samples such that the probability of drawing identical samples from two vectors is equal to their min-max similarity. • Method draws three random variables 𝑟𝑖,𝑗~𝐺𝑎𝑚𝑚𝑎(2,1), 𝑐𝑖,𝑗~𝐺𝑎𝑚𝑚𝑎 2,1 , 𝛽𝑖,𝑗~𝑈𝑛𝑖𝑓𝑜𝑟𝑚(0,1) and then computes 𝑦𝑖,𝑗 = exp 𝑟𝑖,𝑗 log 𝑉𝑖 𝑟𝑖,𝑗 + 𝛽𝑖,𝑗 − 𝛽𝑖,𝑗 which is used as input to the random hash value generation. 6
7. HistoSketch • We propose a new method to compute 𝑦𝑖,𝑗 𝑦𝑖,𝑗 = exp(log 𝑉𝑖 − 𝑟𝑖,𝑗 𝛽𝑖,𝑗) • and show that this method is 1) correct and 2) scale-invariant. Sketch creation 𝑎𝑖,𝑗 = 𝑐𝑖,𝑗 𝑦𝑖,𝑗exp(𝑟𝑖,𝑗) 7 Sketch element Sj Histogram V 0.7 0.6 0.14 0.21 0.41ai,j 3 0.14 The corresponding hash value Aj Computing hash values 1 2 3 4 5i = Minimum 1. compute 𝑦𝑖,𝑗 2. compute hash value 𝑎𝑖,𝑗 3. set 𝑆𝑗 = 𝑎𝑟𝑔𝑚𝑖𝑛 𝑖∈ℇ 𝑎𝑖,𝑗 4. set 𝐴𝑗 = 𝑚𝑖𝑛 𝑖∈ℇ 𝑎𝑖,𝑗
8. HistoSketch Incremental Sketch Update Computation of sketch 𝑆 𝑡 + 1 relies only on 𝑆(𝑡) (with its corresponding hash values 𝐴 𝑡 ), an incoming element 𝑥𝑡+1 and the weight decay factor 𝜆. 8 Sketch element Sj(t) 3 0.147Adjusted hash value Aj(t)e-λ 1 2 3 4 5i = Step II. Add xt+1 - 0.142 - - -ai,j Computing hash value for i 1 2 3 4 5i = Adjusting sketch Sketch element Sj(t+1)2 0.142 Hash value Aj(t+1) Step I. Scale V(t) by e-λ Step III. Update sketch 0.14Original hash value Aj(t) 0.14×1/(e-λ) Minimum 1. scale existing elements in A 2. add 𝑖′ to histogram 3. recompute 𝑎𝑖′ ,𝑗 4. update sketch 𝑆𝑗 and hash values 𝐴𝑗 with minimum 𝑎𝑗
9. Experimental Evaluation • Classification task • Given labeled streaming histograms, classify those histogram instances without label • KNN classifier takes data in the form of sketches for classification with 𝐾 = 5 • KNN takes most up-to-date training data for classification from continuously updated sketches 9
10. Experimental Evaluation • Synthetic dataset • Generated from two Gaussian distributions representing two classes • Simulate data streams with concept drift • Abrupt: one stream starts to receive all elements from the other distribution • Gradual: one stream starts to receive elements from the other distribution with increasing probability, and the labels change • Criteria: 1. How well is the similarity approximated? (impact of sketch length K) 2. How fast can it adapt to concept drift? (impact of weight decay factor λ) 10
11. Experimental Evaluation 1. Impact of sketch length K • Fix 𝜆 = 0.02 and vary 𝐾 = [20, 50, 100, 200, 500, 1000] • Compare against two methods that retain the full histograms: • Histogram-Classical with unweighted elements • Histogram-Forgetting with gradual forgetting weights • A sketch length of 𝐾 = 500 is sufficient to approximate Histogram- Forgetting 11
12. Experimental Evaluation 2. Impact of weight decay factor λ • Fix 𝐾 = 100 and vary 𝜆 = [0, 0.005, 0.01, 0.02, 0.05, 0.1] • Compare against Histogram-LatestK which builds a histogram from the latest 𝐾 = 100 elements in the stream (unweighted) • Similarity computation time: • HistoSketch: 13ms • Histogram-LatestK: 133ms 12
13. Experimental Evaluation • POI dataset • Infer a place’s category from its customers’ visiting pattern • Foursquare dataset: user check-ins for two years from NYC, TKY, IST • Data: user-time visit pairs discretized to the 168 hours in a week • Comparised methods: • Histogram-Coarse: discretized time slots are considered as histogram elements • Histogram-Fine-Classical: user-time pairs are considered as histogram elements • Histogram-Fine-LatestK: only latest K histogram elements • Histogram-Fine-Forgetting: gradual forgetting weights (𝜆 = 0.01) • POISketch: unweighted sketching method that approximates Histogram-Fine-Classical • HistoSketch: approximates Histogram-Fine-Forgetting (𝜆 = 0.01) • Fix 𝐾 = 100 13
14. Experimental Evaluation Classification accuracy 14
15. Experimental Evaluation Runtime performance: classification time 15
16. Conclusion • We introduced HistoSketch, an efficient similarity preserving sketching method for streaming histograms with concept drift. • We demonstrated the effectiveness in approximating normalized min-max similarity. • We use incremental updates to the sketches with gradual forgetting to adapt to concept drift. • We showed on both synthetic and real-world data sets that this method effectively and efficiently approximates similarity and adapts to concept drift. • We observed a speed-up of 7500x on classification with a small loss of accuracy of around 3.5%. 16 Thank you!
17. Backup: Histogram Similarity • Min-max similarity 𝑆𝑖𝑚 𝑀𝑀 𝑉 𝑎 , 𝑉 𝑏 = Σ𝑖∈ℰmin(𝑉𝑖 𝑎 , 𝑉𝑖 𝑏 ) Σ𝑖∈ℰmax(𝑉𝑖 𝑎 , 𝑉𝑖 𝑏 ) • …normalized: sum-to-one normalization 𝑖∈ℰ 𝑉𝑖 𝑎 = 1, 𝑖∈ℰ 𝑉𝑖 𝑏 = 1 • The collision probability between two sketches 𝑆 𝑎, 𝑆 𝑏 is exactly the normalized min-max similarity between 𝑉 𝑎, 𝑉 𝑏 Pr 𝑆𝑗 𝑎 = 𝑆𝑗 𝑏 = 𝑆𝑖𝑚 𝑁𝑀𝑀 𝑉 𝑎, 𝑉 𝑏 17
18. Backup: HistoSketch Implementation • Former histogram 𝑉 𝑡 is required to compute 𝑉(𝑡 + 1) • The previous histogram is maintained in a modified count-min sketch 𝑄 • We extend the count-min sketch with decay weights by scaling all counters 𝑄(𝑡) ∙ 𝑒−𝜆 • Parameter configuration: 𝑑 = 10, 𝑔 = 50 guarantees an error of at most 4% with probability 0.999 18
19. Backup: Experimental Evaluation Classification accuracy over time 19
20. Backup: Future Work • Way to compute 𝑎𝑖,𝑗 can be further simplified • Applications to other domains: e.g., recommendation, community detection 20


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Company Profile - Reverie | Electrical Substation | Electrical Grid
saveSave Company Profile - Reverie For Later
EHVSystem
Company Overview Our Services Company Culture Personal Profile of Company Personnel Instrument Facilities Completed and Awarded Projects Office Location
The company started is journey as a partnership company named as Power & Automation Engineering. The company set its journey as limited company named as Reverie Power & Automation Engineering Ltd. Reverie Power & Automation Engineering Ltd. from October 2011 was established with a clear vision to be a significant player in HV/MV/LV Substations solutions business in Bangladesh and out of the boundary of Bangladesh. Through the enhanced quality, innovative applications and expertise, we position ourselves at the forefront of technology and competitiveness. The company is promoted by a group of professionals who are highly experienced and renowned in the power market and automation field.
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Md. Zahid Hossain is one of the pioneers of International Sea transportation business in Bangladesh. He also has profound experience in other business field in Bangladesh.
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(B.SC in ME, BUET)
Md. Amir Khasru is one of the legends in the power system of Bangladesh. In his career he has been the project director and technical leader in numerous power stations. He has experience of Gas Turbine, Steam Turbine, Gas engine and HFO engine based substation.
Project Director of 5 nos. 50+ MW Power Station Construction for UPGD Technical Director of 5 nos. 50+ MW Power Station O&M for UPGD Director (Technical), PGCB (2006-2007) General Manager, Planning & Development, PGCB (2005-2006) Deputy General Manager, System Protection & Metering Circle (2003-2005) Superintending Engineer, System Protection & Metering Circle BPDB (1996-2003) Director, Load Despatch Center, BPDB (1995-1996) Superintending Engineer, Grid Maintenance Division, BPDB (1994-1995) Govt. Delegate for BIMSTEC, SARI/E summit conference (2006-2007)
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Prof. Dr. S. I. Khan has been a leading researcher in the power system of Bangladesh. He has been the consultant of many power plant and substation projects. He has also experience in renewable energy development system.
Director, Centre for Energy Studies, BUET, July 2005 - February 2009. Dean, Faculty of Electrical & Electronic Engineering, BUET, August 2001-August 2003. Head, Department of Electrical & Electronic Engineering, BUET, August 99- Aug. 01. Professor, Department of Electrical & Electronic Engineering, BUET, since Sept. 95 Worked as Assistant Engineer, System Planning Diroctorate in Bangladesh Power Development Board, February 1977 -April 1981.
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(BSc. in EEE, AUST)
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(BSc. in EEE, BUET)
Md. Ariful Hoque Shuhan is specialized to work in substation automation and remote communication system. He has successfully completed many projects of AMR & SCADA. Power Cell Remote Metering Project, DESCO 10 SS SCADA Project, DPDC 4000 HT AMR Project are the highlighted completed projects in his career. Besides, he has proven ability in working with remote monitoring system and PGCB NLDC SCADA system.
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Member, Institute of Engineers, Bangladesh Member, IEEE, Bangladesh Member, Engineers Association, Tangail
Was Committee Member of the Greater Dhaka Power Distribution and Flow Study committee formed by Ministry of Power, Energy & Mineral Resources, Bangladesh Completed Works
Power Cell Remote Metering Project, All over Bangladesh, Nov2009June2011 DPDC 4000 HT Meter RMR Project, All over Dhaka, Feb2010 to June2011
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Energis 100MW Power Plant 132/11 kV Substation Testing & Commissioning with Fibre optic communication, Chittagong, Dec2010 Everest 15MW Power Plant 33/11kV Substation Testing & Commissioning, Meghnaghat, Sep2010 TK Chemical 132/11kV Substation Testing, Chittagong, Nov2010 DPDC 10 no. 33/11kV Substation testing and commissioning project, all over Dhaka, May2010 to June-2011 Precision Energy 50MW Power Plant 132/11kV SS testing, Ashuganj, April2010 230kV Over Head Line Commissioning with Fibre optic protection scheme, Max Power 100MW Ghorashal, April2011 Summit 50MW Power Plant Transformer Commissioning, Bhulta, Aug2011 DESCO 8 no. 33/11kV Substation testing and Commissioning with substation SCADA, Dec2010 to Sep2011 United 53MW Power Plant 230kV Bay commissioning with fiber optic UG line protection, Sep2011 Hosaf 20MW Power Plant 33/11kV Substation testing and commissioning, Bogra, August2010 DESH Energy 100MW Power Plant 132/11kV SS testing & commissioning including fiber optic UG cable protection, Shiddhirganj, Feb2011 RPCL 50MW Power Plant 33/11kV SS Testing and Commissioning, Gazipur, Dec2011 Field Equipment testing at Hosaf 50MW Power Plant 132/11kV SS, Sylhet, Dec2011
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Md. Jahangir Al Jilani has expertise on Generator Synchronization and its associates. He is also capable to work in design, fabrication & installation of LV(11/0.44kV) Electrical Substation upto 10MVA. He has the proven capabilities to Synchronize Diesel-Diesel, gas-gas & DieselGas generators upto 11kV system. He has also expertise in remote monitoring & control of Generators.
Worked as Asst. Consultant in Nippon Koei Co. Ltd. Under one of JICA project on Solar PV system & Biogas Technology. Worked as Senior Officer in Rahimafrooz Energy Services Ltd. for 3 Years. Affiliation:
Member, Institute of Engineers, Bangladesh Member, IEEE, Bangladesh Member, ROCA(Rangpur Old Cadet Association)
Email: jilani@reverie-bd.com, Phone: 01716438379
Design, Supply, Installation & Commissioning of 1250KVA 11/.415KV Electrical Substation of Evergreen Industries Ltd at Tejgaon. Design, Supply, Installation & Commissioning of 1000KVA 11/.415KV Electrical Substation of MAX Industries Ltd at Manikgonj. Design, Supply, Installation & Commissioning of 500KVA 11/.415KV Electrical Substation of Hashim Developer Ltd at Tejgaon. Design, Supply, Installation & Commissioning of 315KVA 11/.415KV Electrical Substation of Jamila Tenary Ltd at Manikgonj. Design, Supply, Installation & Commissioning of 315KVA 11/.415KV Electrical Substation of Winner Packages & Accessories Ltd. at Saver. Design, Supply, Installation & Commissioning of 200KVA 11/.415KV Electrical Substation of BDM Hospital at Mohammadpur. Design, Fabrication of many Automatic Transfer Switches. Range: 20A-1600A Design a system for automatic start operation of manually operated generators. Design, Supply , Installation & Commissioning of Synchronizing Panel for 1x509KVA+1x750KVA+1x880KVA gen sets of Concord IK Tower at Gulshan-2.
Design, Supply , Installation & Commissioning of Synchronizing Panel for 2x509KVA gen sets of Rangs Tower at purana Paltan. Installation & Commissioning of Synchronizing Panel for 3x1MW gensets of Nasir Group Industries. Installation & Commissioning of Synchronizing Panel for 2x500KVA gen sets of BRAC Centre. Design, Supply, Installation & Commissioning of 20KWp Solar System of Biswas Builders Ltd. at New Market City Complex. Design, Supply, Installation & Commissioning of 1KWp Solar System of DK Holdings Ltd. at Rayer Bazar. Design, Supply, Installation & Commissioning of 1.14KWp Solar System of Glory Homes Ltd. at Jigatola. Design, Supply, Installation & Commissioning of 215Wp Solar System of Dr. Nurul Hoque Ltd. at Mirpur.
Power Cable Stand (1) Cable Glider (10) Ferrule Printer (1) Hydraulic Punch Tools for Power Cables (2) Lifting Tools, Upto 10 Ton (1) Blow Lamp (5) Heat Gun (3) Hammer Drill Machine (3) Column Drill Machine (1) High Speed Cutter (1) Hand tools (i.e. Spanners, ratchets etc.)
Primary Equipment Tester (CPC-100, Omicron, Austria) Secondary Equipment Tester (CMC356Omicron, Austria) Breaker Analyzer (CBA-1000, ISATest, Italy) Secondary Injector, Single Phase (T&R, UK) Primary Current Injector (2000A, Pulsar, Bangladesh) Earth Resistivity Tester (Megger, UK) 5kV Insulation resistance tester (Kyoritsu, Japan) Primary Current Injector, Pulsar, Bangladesh Mili-Amp and High Range Clip On (Kyoritsu, Japan) AVO Meter (Fluke, USA)
Sl. No. 1. 2. 3. 4. 5. 6. 7. Project Name Supply & Installation1000KVA Electrical Substation Supply & Installation 500KVA Electrical Substation Supply & Installation 500A DB Panel 1250kVA 11/0.415kV Substation Supply & Installation 315KVA Electrical Substation 20KWp Solar System Supply & Installation 315KVA Electrical Substation Customer Details MAX Industries Ltd. Site: Jaigir, Manikgonj Hashim Devloper Ltd. Site: 205/1A, Tejgaon I/A MAX Industries Ltd. Site: Jaigir Manikgonj Evergreen Industries Ltd. Jamila Tenary Ltd. Site: Hajaribagh Biswas Builders Ltd. Customer Contact Md. Moin-Manager,Commercial Md.Hashim Al Vuyia, Managing Director Md. Moin-Manager,Commercial. Mr. Shasuzzoha, Manager Mr Sisir Rai-01711008720 Mr. Bishwa, Phn: 01730093346
Winner Packages & Mr Jakir Hossain, Manager Accessories Ltd.Savar Dhaka.
Our Projects (Contd..)
Sl. No. 8. 9. 10. Project Name 11KV/33KV Substation of 50 MW Power Plant testing & Commissioning 11KV/132KV Substation of 50 MW Power Plant testing Supply of 18 Nos. of PFI, ATS DB at Haripur 450MW CCPP Project Customer Details 50 MW PP RPCL, Gazipur 50MW PP Energy Prima, Fenchugonj Hyundai Construction & Engineering Co. Ltd. Customer Contact Md. Kamruzzaman, ManagerElectrical, RPCL Mr. Rokonuzzaman, Plant Manager, EPCL J P Moon, Construction In Charge
11/33kV Substation of 25 MW Power Plant Erection, Testing & Commissioning. Supply & Installation 200KVA Electrical Substation 11/132kV Substation of Baghabari 50MW Power Plant Testing Supply and Installation of 100kVA Substation
25MW PP RPCL, Rauzan, Chittagong
BDM Hospital CCC Engineering Ltd. Basic Builders Ltd.
Md. Kamruzzaman, ManagerElectrical, RPCL
Md. Mosharraf Hossain, Managing Director Chinguan Chen Chief Engineer Humayun Kabir Supervisor
Sl. No. 15. 16. 17. 18. Project Name Customer Details Customer Contact Nazir Ahmed, Manager, Suvastu Mr. Shahed, Accounts Manager Mr. Ashfaq Nazir Ahmed, Manager, Suvastu Alamgir Hossain, Director Mr. Khokon Managing Director Supply of 800A ATS at Suvastu Nazar Suvastu Developers Ltd. Valley Project Supply and Installation of 315kVA Substation Supply and Installation of 315kVA Substation Supply and Installation of 100kVA Substation Supply and Installation of 500kVA Substation Supply and Installation of 200kVA Substation Bangladesh Olympic Association RRMM Leather, Hazaribag Suvastu Developers Ltd.
Shajan Housing Ltd.
Harun Twisting Industries Ltd.
Reverie Power & Automation Engineering Ltd. 260/B, Tejgaon Industrial Area, Dhaka1208, Email: info@reverie-bd.com, Phone: +8801712225056 Fax: +880-2-8811590, Website: www.reverie-bd.com
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Amazon.co.uk: Welcome to the Canteen: Explore similar items
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Traffic Live ... that ought to be enough 27 Jun 2004Reviewer: Juan Mobili (see more about me) (Valley Cottage, NY USA)This was a contractually obligated album and a period of transition too, and it's also Traffic best moment live.Soon "Welcome to the Canteen" would be followed by "The Low Spark of High Heeled Boys," showing most people that the band was not dead, although by "Shoot Out at the Fantasy Factory" they... Read More
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Amazon.com : Baby Aspen "My Little Night Owl" Snuggle Sack and Cap, 0-6 months : Infant And Toddler Sleepsacks : Baby
Baby Aspen "My Littl...
This item: Baby Aspen "My Little Night Owl" Snuggle Sack and Cap, 0-6 months $25.20
Baby Aspen Baby "Let the Fin Begin" Blue Terry Shark Robe, Blue, 0-9 months $16.62
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$16.62 Prime Baby Aspen, My Little Night Owl Plush Velour Baby Blanket, Beige, One Size
$15.52 Prime Summer Infant SwaddleMe Pure Love Adjustable Infant Wrap , 7-14 pounds,Owl
$11.37 Prime Baby Aspen, Snug As a Bug Ladybug Snuggle Sack, 0-6 Months
$25.31 Prime Baby Aspen, My Little Night Owl Five-Piece Baby Gift Set
$27.08 Prime Baby Banana Bendable Training Toothbrush, Infant
Buy "Baby Aspen "My Little Night Owl" Snuggle Sack and ...” from Amazon Warehouse Deals and save 42% off the $36.00 list price. Product is eligible for Amazon's 30-day returns policy and Prime or FREE Shipping.
Features enchanting, owl-inspired snuggle sack and cap with plush, ribbed fabric outside and comfy-cozy 100% cotton inside. Snuggle sack is ribbed, camel-colored polyester with rich-brown jumper-like fit at the top, an applique that says My Little Night Owl,a round, gold tummy with white, diamond-shaped accents and a plush, green wing on each side. Easy open-and-close snaps at the shoulders and zipper on the bottom for quick access. Matching cap has owl-like ears, two wide eyes, an orange beak and crest and braided-fabric ties with fuzzy balls on the ends Snuggle sack and cap arrive on a tree branch-shaped cardboard hanger. Machine-wash cool and line dry.
Product Dimensions23 x 14 x 24 inches
UPC887662845053 843905060072 640522571628
Item model numberBA15002NA
Material Type100% Polyester; Lining: 100% Cotton
ASINB004071ZO4
#4,404 in Baby (See top 100)
23 of 24 people found the following review helpfulAdorable, with one flaw
on March 17, 2011Color: Beige
Desiree Meisner
on January 14, 2014Color: Beige
This was the first item I requested on my registry because it looked so adorable, Once I received it, I couldn't have been more pleased, It's constructed well, very warm and decently thick. Perfect for my house in winter! My baby is 6 weeks but fits the 3-6mo perfectly.. which is normal for him. Great product!
8 of 8 people found the following review helpfulcute... but
livnleo
on October 29, 2011Color: Beige
The costume is made with nice material. The only downside to it is that the head piece is EXTREMELY small. It would fit a baby under 12 lbs. I had to have it altered in order for it to fit and unfortunately its still pretty small.
10 of 11 people found the following review helpfulOwl Suit
on September 29, 2011Color: Beige
Precious outerwear outfit. Quality material and workmanship. WE are using at a Halloween costume too. My grandson doesn't give a hoot but we think its cute.
9 of 10 people found the following review helpfulAlmost Perfect Owl Costume for Infant Boy
on October 10, 2011Color: Beige
I adore this snuggle sack. It's not only cute but made very well- durable, soft, "nice" materials & details. My ONLY complaint is the cap (a major part of this when worn as a costume). The cap runs a little small and is not stretchy. I am not sure how well it will fit by the time my baby outgrows the sack, but I still love it.
Katrina Vojacsek
on March 8, 2011Color: Beige
The ribbed texture of the fabric (despite being 100% polyester) is very nice and it is extremely soft, snuggly, On the whole it appears well made of quality materials. Unlike the other reviewer I could see using it as a sleeper (but then we are not in sunny CA-lol). The inside is fully lined (100% cotton) with the same yellow polka dot/diamonds fabric as on the tummy and the outside is pretty light and fluffy so it doesn't seem too warm to me, but I guess it depends on your climate, the time of year and where you keep your thermostat? I would recommend drying inside out as the outside fabric seems to dry faster than the cotton lining. The top snaps at both shoulders and has maroon felt like "buttons" sewn securely over the snaps which you can't see pictured very well-but I find very cute. I liked how the bottom unzips (smooth, quality zipper) to easily change baby. For being 0-6 mos the sack part is larger than I expected-nice and roomy. I agree with the other reviewer that the hat looks a little small (especially considering my kids tend to have larger than average heads) but I am scheduled to be induced tomorrow so will update the review if I have issues with it fitting. But I think it may look small when compared to the sack-its a good size. The beak is also a felt-type material, the eyes are a soft white velour-like material. I must have missed the part about it "arrives on a tree branch-shaped CARDBOARD hanger" there is nothing very special about the hanger, but I think I was expecting too much there-like a real tree branch or something. All in all, I love the owl themes are back, thought this was so cute when I saw it and it arrived exactly as pictured. I'm very pleased and would recommend it to others.
on May 6, 2013Color: Beige
After the first time washing the hat part totally came apart. One of the side tufts came off and so did part of the owl "beak".
2 of 2 people found the following review helpfulAdorable and Perfect for 1st photos!
Justanotherhumanmommy
on June 18, 2013Color: Beige
Even better than as described the material is super soft and it is extremely well made. I was not sure my daughter would be a girl so this hit the gender neutral nail on the head while still being the cutest owl outfit around. I purchased "Out of box" and the item was literally in brand new condition. My daughters first pictures came out amazing! The shipping was so fast it arrived the next day! Also, I love she can wear this up to 6months. Great investment in cuteness.
Published 25 days ago by JasyBB
Published 27 days ago by Lingyu Chi
Cousin loves it for her baby
Cutest thing ever. I wish they had these is adult sizes 
Easy to get it on my nephew and he looks adorable in it. My only problem with this suit (just like countless other buyers) is the hat, now my nephew was 3 months old when I got...
Published 1 month ago by Sami Baig
It was too small for my 3 month small baby (10%percentile) especially the hat
Worth the money. Supersoft, warm, and comfy for my little one.
Published 2 months ago by Rebecca W
Published 2 months ago by Wei Siyuan
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