Thermal Road Repair
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TRR provides an asphalt repair system that uses controlled heat to repair potholes, trenches, and other defects in asphalt surfaces. The system agitates and heats the existing asphalt to blend in new material, producing jointless repairs. Key advantages are significant savings on emissions and materials through recycling asphalt, as well as eliminating future maintenance needs since there are no formed edges. TRR works with contractors to provide equipment, support, training, and completed repairs for clients like utility and highway agencies.
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FAROOQ CV, PRODUCTION, QUALITY, PLC AUTOMATION,.pdf
Curriculum Vitae
Scope:
• PLASTIC PIPE PRODUCTION, QUALITY, SPECIALIST
• Plc & Automation Specialist for Plastic Pipe Industries
FAROOQ RASHEED ANSARI FROM PAKISTAN
Achievements Summary
23 Years of dedicated and focused working Field Experience in the Plastic pipe industry,
Experienced as a Plastic Pipe Technologist.
Regarding Production units:
• Experience with 10 mm to 1200 mm PIPE Size Production at Different Production Units
Regarding Production:
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2. MD, LD, HDPE (Water and Gas),
3. UPVC PVC, CPVC, UPVC Electrical and Telecom Conduit Pipes
4. PPRC, PEX, PERT, PP-RCT, UV & Composite & Solid Multi-Layer Pipe and Fittings,
5. COD, Micro-Duct, Electrical and Telecom Conduit Pipes, Spiro PIPE, etc.
Regarding Department:
• Production, Quality, and Maintenance
Regarding international Production and Quality Standards.
• DIN, ISO, Astm, BS, EN, SE CO, SASO, GSO (STC, ZAIN, MOBILY)
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1. PPR: DIN 8077/78, ISO 15874, DIN 16962
2. PEX: ISO 15875, DIN 16892/93, ASTM 2788
3. PERT: ISO 22391-1,2
4. Multilayer piping system: ISO 21003, TSE K 28, TS 13715
5. PPMD: EN 1451, EN 14758
6. PE: 4427, 4437,
7. UPVC PRESSURE: ASTM D1785, D2665, BS 3505, DIN 8062, BS EN 1452,
8. UPVC NON-PRESSURE: BSEN 1401, BSEN 1329, DIN 19534, BS EN 4514, BS 5255, AUS/NZ 1260,
9. UPVC CONDUITS & DUCTS: BS 4607, BS 6099
10. CPVC: ASTM F441
Regarding international Quality testing Standards.
• DIN, ISO, Astm, BS, EN, SE CO, SASO, GSO.
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1. Dimensions - DIN EN ISO 3126:2005-05
2. Density test - ISO 1183
3. MFI- ISO 1133, D1238
4. Fiber impact - ISO 11173, EN 1411
5. Thermal expansion - DIN 53752
6. Heat Reversion - ISO 2505
7. HPT - ISO 1167
8. Ppr impact - ISO 9854-1,2, DIN 8078:2008, pendulum ISO 179-1
9. Pex crosslinking - ASTM 2765
10. LTE - DIN 53752
11. Homogeneity - ISO 18553:2002-03
12. Drainage Fittings Impact test - ISO 13263
Brass PPR Fitting related.
1. Brass and gauge dimensions - EN 10226-1,2,3
2. Brass thread- DIN 2999
3. Brass testing - EN 1254-3
4. Brass/Steel material related - EN 10088-1, BS 2767
5. 13263, 13245, 13257 13257, 13259,
Regarding international accreditation organizations:
SKZ, WRAS, DVGW, BQSR, SGS, SABER, NSF, AENOR, KIWA,
Experience with (10 mm to 1200 mm PIPE Size) Different Production lines like
• battenfeld-cincinnati
• Kabra Extruder
• KraussMaffei
• BASAR MAKINA
• SICA
• TECNOMATIC
• Rollepaal
• Reifenhäuser
• Fangli,
• Jwell
• Jinhu
• DAKUMAR
• BEIER EXTRUDER
• Tongda machinery
• MARKEM IMAJE
• CONPRO. Gravimetric System
• Inoex Saveomat M2 Automatic Wall Thickness & Weight Controller.
• SAP & ORACLE ERP NETSUITE (IMS, ERP)
• LEAN SIX SIGMA, OEE, TPM, PDCA, DMAIC, 6S,
Have great experience & skills with
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5 Steps to Achieve More CostEffective Aminebased Carbon Capture Processes at ...
5 Steps to Achieve More CostEffective Aminebased Carbon Capture Processes at ...
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FAROOQ CV, PRODUCTION, QUALITY, PLC AUTOMATION,.pdf
The Role of Gas Seal Technology In Reducing Methane Emissions
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
官方认证美国密歇根州立大学毕业证学位证书原版一模一样
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International Conference on NLP, Artificial Intelligence, Machine Learning an...
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Computational Engineering IITH Presentation
This Presentation will give you a brief idea about what Computational Engineering at IIT Hyderabad has to offer.
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Unit-III-ELECTROCHEMICAL STORAGE DEVICES.ppt
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
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Thermal Road Repair
- 1. A Partnership in Technology and Delivery Asphalt Repair Technology All information, processes and designs in this presentation are sent commercially in confidence and is subject to UK Patent Application no: GB1413765.7. Please do not distribute without the permission of the sender.
- 2. System Development - Repairs TRR have targeted the main problems with surface course asphalt Joint failures Texture loss Potholing Desire for a cost effective “First Time Reinstatement Option” Utility trench density and air void failures Providing jointless paving
- 4. How does our process work? We use a like for like material to the treatment area Heater is positioned over the repair area Controlled heat is pulsed into the surface at controlled depth Surface material is agitated to level the treatment area New material added (where required) and mixed Treatment area is compacted
- 5. System Advantages National Highways Sector Scheme approved – NHSS 13 Significant savings on carbon emissions, on delivery and use of recycled materials Elimination of any formed edge, negating future maintenance issues Heat controlled by computer system giving constant heated material, which will help with compaction and quality of surface finish. Non aggressive action of repair, no saw cutting, no hydraulic breakers, no milling. Kit can achieve high outputs - 100 l m to 200 l m per shift
- 6. What does our process deliver? Within spec High quality reworks options Hand over jointless repairs, trenches and potholes Complete efficient surface repairs Low cost Aesthetically sound
- 7. Contractor Delivers TRR Equipment TRR Support TRR Training How does it work – ‘Non Operated Hire’?
- 9. Resolution of air void failures Client Site Date Untreated Reworked Improvement Location Wigan MBC Makerfield Way, Wigan Jul-14 18.40% 5.60% 12.8% Carriagewa y ENW Lowland Way, Bispham May-15 16.70% 10.80% 5.90% Footway ENW Clifton Drive North, St Annes May-15 16.40% 12.80% 3.6% Footway ENW Clifton Drive North, St Annes May-15 19.6%* 13%* 6.6% Footway All testing has been independently carried out by a UKAS accredited laboratory (PTS)
- 10. Air void defect rework
- 11. October 2014, A51 Tarpoley Cheshire (Client Cheshire West and Chester) Completed Works
- 12. April 2016, CWAC (Client Ringway) Completed Works
- 13. May 2015, Moorhouse, Carlisle, Cumbria (client ENW) Completed Works
- 14. August 2014, Crewe, Cheshire (client Cheshire East) Completed Works
- 15. What is your current cost of delivering repairs that fail? Minimising return visits to site Increasing work output How long will your conventional reinstatement last? Cost Savings
- 16. Case Study Actual Environmental Benefits from Recently Completed Electricity North West Scheme (690m2)
- 17. Awards
- 18. Current Clients
- 19. Contact details: Danny.falls@thermalroadrepairs.com 07710 701 348 Aidan.Conway@thermalroadrepairs.com 07971 960 301 Contact details