Về kỹ thuật Attention trong mô hình sequence-to-sequence tại hội nghị ACL 2017Minh Pham
Trình bày về kỹ thuật attention trong mô hình sequence-to-sequence và ứng dụng trong các nghiên cứu NLP tại ACL 2017. Ngoài ra chúng tôi cũng tóm tắt một số các nghiên cứu thú vị khác tại hội nghị.
Đây là hướng dẫn chi tiết và đầy đủ nhất về cách thức xây dựng từ điển năng lực. Slide này được viết và đúc rút ra từ hơn 1 năm đau đầu tham gia dự án xây dựng của công ty.
ConvNeXt: A ConvNet for the 2020s explainedSushant Gautam
Explained here: https://youtu.be/aBvDPL1jFnI
In Nepali
A ConvNet for the 2020s (Zhuang Liu et al.)
ComvNeXt paper
Deep Learning for Visual Intelligence
Sushant Gautam
MSCIISE
Department of Electronics and Computer Engineering
Institute of Engineering, Thapathali Campus
13 March 2022
To all the authors (obviously!!)
1. Jinwon Lee's slides at https://www.slideshare.net/JinwonLee9/pr366-a-convnet-for-2020s?qid=274bc524-23ae-4c13-b03b-0d2416976ad5&v=&b=&from_search=1
2. Letitia from AI Coffee Break: https://www.youtube.com/watch?v=SndHALawoag
I even edited some of her hard visual works and put them as a slide. :(
Building and Evolving a Dependency-Graph Based Microservice Architecture (La...confluent
With the rising adoption of stream- and event-driven processing microservice architectures are becoming more and more complex. One challenge that many businesses face during the initial and ongoign development of these solutions is how to properly model and maintain dependencies between microservices. One specific example for this that will be used throughout this talk cleansing and enrichment of data that has been ingested into a streaming platform. For most use cases there are a lot of minor tasks that need to be performed on every piece of data before it is fully usable for processing. Some common examples are: normalize phone numbers, normalize street addresses, geocode addresses, lookup customer data and enrich record, ... Most of these tasks are completely independent of each other, but some have dependencies to be run before or after other tasks - geocoding, for example, should be done only after address normalization has finished. Defining and orchestrating a complex graph of these operations is no small feat. This talk will focus on outlining the requirements and challenges that one needs to solve when trying to implement a flexible framework for solving this use case. It will then build on these requirements and present the blueprint of a generic solution and show how Kafka and Kafka Streams are a perfect fit to address and overcome most challenges. This talk, while offering some technical details is mostly targeted at people at the architecture, rather than the code, level. Listeners will gain a thorough understanding of the challenges that stream processing offers but also be provided with generic patterns that can be used to solve these challenges in their specific infrastructure.
Về kỹ thuật Attention trong mô hình sequence-to-sequence tại hội nghị ACL 2017Minh Pham
Trình bày về kỹ thuật attention trong mô hình sequence-to-sequence và ứng dụng trong các nghiên cứu NLP tại ACL 2017. Ngoài ra chúng tôi cũng tóm tắt một số các nghiên cứu thú vị khác tại hội nghị.
Đây là hướng dẫn chi tiết và đầy đủ nhất về cách thức xây dựng từ điển năng lực. Slide này được viết và đúc rút ra từ hơn 1 năm đau đầu tham gia dự án xây dựng của công ty.
ConvNeXt: A ConvNet for the 2020s explainedSushant Gautam
Explained here: https://youtu.be/aBvDPL1jFnI
In Nepali
A ConvNet for the 2020s (Zhuang Liu et al.)
ComvNeXt paper
Deep Learning for Visual Intelligence
Sushant Gautam
MSCIISE
Department of Electronics and Computer Engineering
Institute of Engineering, Thapathali Campus
13 March 2022
To all the authors (obviously!!)
1. Jinwon Lee's slides at https://www.slideshare.net/JinwonLee9/pr366-a-convnet-for-2020s?qid=274bc524-23ae-4c13-b03b-0d2416976ad5&v=&b=&from_search=1
2. Letitia from AI Coffee Break: https://www.youtube.com/watch?v=SndHALawoag
I even edited some of her hard visual works and put them as a slide. :(
Building and Evolving a Dependency-Graph Based Microservice Architecture (La...confluent
With the rising adoption of stream- and event-driven processing microservice architectures are becoming more and more complex. One challenge that many businesses face during the initial and ongoign development of these solutions is how to properly model and maintain dependencies between microservices. One specific example for this that will be used throughout this talk cleansing and enrichment of data that has been ingested into a streaming platform. For most use cases there are a lot of minor tasks that need to be performed on every piece of data before it is fully usable for processing. Some common examples are: normalize phone numbers, normalize street addresses, geocode addresses, lookup customer data and enrich record, ... Most of these tasks are completely independent of each other, but some have dependencies to be run before or after other tasks - geocoding, for example, should be done only after address normalization has finished. Defining and orchestrating a complex graph of these operations is no small feat. This talk will focus on outlining the requirements and challenges that one needs to solve when trying to implement a flexible framework for solving this use case. It will then build on these requirements and present the blueprint of a generic solution and show how Kafka and Kafka Streams are a perfect fit to address and overcome most challenges. This talk, while offering some technical details is mostly targeted at people at the architecture, rather than the code, level. Listeners will gain a thorough understanding of the challenges that stream processing offers but also be provided with generic patterns that can be used to solve these challenges in their specific infrastructure.
Kaseya Connect 2012: BEST PRACTICE SYSTEMS MANAGEMENT IN REGULATED BUSINESSESKaseya
Learn how customer, First United Bank, leverages Kaseya in a highly regulated environment, including tips and tricks for leveraging Kaseya Patch Management in conjunction with other modules to help ensure compliance.
Presented by: Kyle Simpson, Systems Administrator, First United Bank
Convolutional Neural Networks (ConvNets) have been at the forefront of deep learning and computer vision tasks in the 2020s. These networks have undergone several advancements and improvements in recent years. Here's an overview of some key components and trends in ConvNets for the 2020s:
Architectural advancements: ConvNets have seen the development of more sophisticated architectures. One notable example is the introduction of residual connections, as seen in the ResNet architecture. Residual connections alleviate the vanishing gradient problem and enable the training of very deep networks.
Attention mechanisms: Inspired by the success of attention mechanisms in natural language processing tasks, ConvNets have incorporated attention mechanisms into their architectures. These mechanisms allow networks to focus on specific regions or features, enhancing their discriminative power. Popular attention mechanisms include spatial attention and channel attention.
Efficient architectures: With the increasing demand for deploying ConvNets on resource-constrained devices, there has been a focus on designing efficient architectures. Models like MobileNet and EfficientNet have been developed, which use depth-wise convolutions, squeeze-and-excitation modules, and neural architecture search techniques to reduce the computational cost while maintaining competitive performance.
Self-supervised learning: ConvNets have benefited from advancements in self-supervised learning techniques. By leveraging unlabeled data, models can learn useful representations through pretext tasks. These pre-trained models can then be fine-tuned on specific downstream tasks, leading to improved performance even with limited labeled data.
Transfer learning: Transfer learning has become a standard practice in ConvNets. Models pre-trained on large-scale datasets, such as ImageNet, serve as a starting point for various computer vision tasks. By leveraging the learned representations, transfer learning enables faster convergence and better generalization on new tasks, even with smaller labeled datasets.
Generative models: ConvNets have been used to develop powerful generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs). GANs have been employed for tasks like image synthesis, super-resolution, and image-to-image translation. VAEs have been utilized for tasks like image generation, anomaly detection, and data augmentation.
Interpretability and explainability: As ConvNets have become more complex, the need for interpretability and explainability has also grown. Techniques like Grad-CAM (Gradient-weighted Class Activation Mapping) and attention maps help visualize which regions of an input image are important for the network's predictions. These approaches aid in understanding the decision-making process of ConvNets.
Continual learning: ConvNets have faced challenges in adapting to new tasks without forgetting previously learned knowledge.
usually we may need to collect field sensors' 4-20mA or 0-5V data to control center, we can use wireless solution when it's hard to wire cables. LS analog input and output module is to collect and log analog signals
The GE8808B is a 3-channel constant current LED drive with resumable data transfers and internal display patterns. There are three open-drain constant current outputs, with a build-in PWM of grayscale. The range of input power is from +9V to +15V, and voltage-endurance of LED port is +12V. There is a built-in 12bits GAMMA correction module. PWM maximum refresh frequency is 8Khz, the GE8808B use the e-RZ (extended return to zero code)as the signal transmission mode,
which can control the output current channel by channel and cascade infinitely,it provides two-signal data input as redundant control, which ensures the transmission of the signal if any single chip damages.In the displays the built-in display patterns that is suitable for those applications without a controller. There is the built-in power-on and power-off protection in the drive,which can enhance the service life of the chip. It alsohas the automatic test function while power on, working environment is from -40 ° C to + 85 ° C.
Kaseya Connect 2012: BEST PRACTICE SYSTEMS MANAGEMENT IN REGULATED BUSINESSESKaseya
Learn how customer, First United Bank, leverages Kaseya in a highly regulated environment, including tips and tricks for leveraging Kaseya Patch Management in conjunction with other modules to help ensure compliance.
Presented by: Kyle Simpson, Systems Administrator, First United Bank
Convolutional Neural Networks (ConvNets) have been at the forefront of deep learning and computer vision tasks in the 2020s. These networks have undergone several advancements and improvements in recent years. Here's an overview of some key components and trends in ConvNets for the 2020s:
Architectural advancements: ConvNets have seen the development of more sophisticated architectures. One notable example is the introduction of residual connections, as seen in the ResNet architecture. Residual connections alleviate the vanishing gradient problem and enable the training of very deep networks.
Attention mechanisms: Inspired by the success of attention mechanisms in natural language processing tasks, ConvNets have incorporated attention mechanisms into their architectures. These mechanisms allow networks to focus on specific regions or features, enhancing their discriminative power. Popular attention mechanisms include spatial attention and channel attention.
Efficient architectures: With the increasing demand for deploying ConvNets on resource-constrained devices, there has been a focus on designing efficient architectures. Models like MobileNet and EfficientNet have been developed, which use depth-wise convolutions, squeeze-and-excitation modules, and neural architecture search techniques to reduce the computational cost while maintaining competitive performance.
Self-supervised learning: ConvNets have benefited from advancements in self-supervised learning techniques. By leveraging unlabeled data, models can learn useful representations through pretext tasks. These pre-trained models can then be fine-tuned on specific downstream tasks, leading to improved performance even with limited labeled data.
Transfer learning: Transfer learning has become a standard practice in ConvNets. Models pre-trained on large-scale datasets, such as ImageNet, serve as a starting point for various computer vision tasks. By leveraging the learned representations, transfer learning enables faster convergence and better generalization on new tasks, even with smaller labeled datasets.
Generative models: ConvNets have been used to develop powerful generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs). GANs have been employed for tasks like image synthesis, super-resolution, and image-to-image translation. VAEs have been utilized for tasks like image generation, anomaly detection, and data augmentation.
Interpretability and explainability: As ConvNets have become more complex, the need for interpretability and explainability has also grown. Techniques like Grad-CAM (Gradient-weighted Class Activation Mapping) and attention maps help visualize which regions of an input image are important for the network's predictions. These approaches aid in understanding the decision-making process of ConvNets.
Continual learning: ConvNets have faced challenges in adapting to new tasks without forgetting previously learned knowledge.
usually we may need to collect field sensors' 4-20mA or 0-5V data to control center, we can use wireless solution when it's hard to wire cables. LS analog input and output module is to collect and log analog signals
The GE8808B is a 3-channel constant current LED drive with resumable data transfers and internal display patterns. There are three open-drain constant current outputs, with a build-in PWM of grayscale. The range of input power is from +9V to +15V, and voltage-endurance of LED port is +12V. There is a built-in 12bits GAMMA correction module. PWM maximum refresh frequency is 8Khz, the GE8808B use the e-RZ (extended return to zero code)as the signal transmission mode,
which can control the output current channel by channel and cascade infinitely,it provides two-signal data input as redundant control, which ensures the transmission of the signal if any single chip damages.In the displays the built-in display patterns that is suitable for those applications without a controller. There is the built-in power-on and power-off protection in the drive,which can enhance the service life of the chip. It alsohas the automatic test function while power on, working environment is from -40 ° C to + 85 ° C.
CMEL 2.8 inch Amoled(240x320) DatasheetPanox Display
Model: C0283QGLA/ C0283QGLC/ C0283QGLD
CMEL 2.8" Amoled is a classical screen which can work well under a low temperature and with a low consumption, but it has been EOL for a long time. Panox Display can produce it with CMEL original cell and IC S6E63D6.
So if you have broken CMEL 2.8"screens which IC can work well, please contact us, we can produce brand new CMEL 2.8" for you.
Range of application: Industrial control, Medical equipment, Electrommunication, Numerical control, Monitor, instrument, Military products.
For more specification of 2.8" Amoled,
https://www.panoxdisplay.com/html/products/amoled-39-1.html
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
2. Contact: Sunny Web: www.lensen-tech.com Email: sunny@lensen-tech.com
1. LS-RDIO series module general introduction
LS-RDIO Modbus RTU is an industrial controller designed by Lensen Technology. It
supports point to point, point to multi-points wireless DI, DO collection and control.
This transceiver has the advantage of easy installation, no program required, no
communication fee. It is reliable and cost-effective substitution for rail signal cable,
slip ring signal wire.
LS-RDIO0202 has 2 inputs 2 relay outputs and 1 RS485 (wireless RS485 is
customized). Power output we have 2 versions, one is 100mW for 1km LOS control
distance, and another one is 1W for 3km LOS control distance
2. Application Field
LS-RDIO series I/O module is widely used in industry automation like factory central
data process, pump control, tank level monitoring, Conveyor Control,oil monitoring,
mining machinery, environment monitoring equipment, lifting devices, robots control,
filling machine, mixing equipment, logistic line, AGV etc.
3. About Item Name
3. Contact: Sunny Web: www.lensen-tech.com Email: sunny@lensen-tech.com
4. LS-RDIO0202 Parameter
Parameter
Power output: 1W (or 100mW version)
Power supply： 9V-30V (usually choose 24V DC or 12V DC)
Control distance: 3km (or 1km version)
Baud rate: 1200bps
Receiving sensibility: -123dBm@1200bps, -118dbm(9600bps)
Networking
Networking: Point to point and point to multipoint
Protocol: Modbus RTU protocol
Port Description
Input: 2 active inputs (passive customize)
Output:
2 passive relay outputs,
contact max load AC250V/5A，DC30V/5A
(active outputs 0-5V customize)
Communication port RS485 (wireless RS485 customize)
Current
Transmit current: <600mA (or 100mA version)
Receiving current: <50mA (or 20mA version)
General
Frequency: 433MHz, 400, 450, 470 or other band
Channel No： 16 channels, can change via DIP switch
Physical Properties
Dimension: 115×90×40(mm)
Antenna Connector: SMA，vehicle antenna with 1.5m cable
Temperature: 35℃~+75℃( industrial)
Mounting method: Standard 3.5 inch industrial guild rail
4. Contact: Sunny Web: www.lensen-tech.com Email: sunny@lensen-tech.com
5. LS-RDIO series I/O control application example
1) Point to point control
Remarks:
Module A Module B Input and Output Correspondence
A（DI1-DI8） B（DO1-DO8） DI1-DI8 of A to control DO1-DO8 of B respectively
A（DO1-DO8） B（DI1-DI8） DO1-DO8 of A is controlled by DI1-DI8 of B respectively
Notes: A and B should use the same channel
5. Contact: Sunny Web: www.lensen-tech.com Email: sunny@lensen-tech.com
2) One master to control 2 slaves
Remarks：
Master Module Slave Module Input and Output Correspondence
A（DI1-DI4） B1（DO1-DO4） DI1-DI4 of A control DO1-DO4 of B1 respectively
A（DI5-DI8） B2（DO1-DO4） DI5-DI8 of A control DO1-DO4 of B2 respectively
A（DO1-DO4） B1（DI1-DI4） DO1-DO4 of A is controlled by DI1-DI4 of B1
A（DO5-DO8） B2（DI1-DI4） DO5-DO8 of A is controlled by DI1-DI4 of B2
Notes: A and B should use the same channel
6. Contact: Sunny Web: www.lensen-tech.com Email: sunny@lensen-tech.com
3) One master to control 4 slaves
Remarks：
Master Slave Input and Output Correspondence
A（DI1-DI2） B1（DO1-DO2） DI1-DI2 of A control DO1-DO2 of B1
A（DI3-DI4） B2（DO1-DO2） DI3-DI4 of A control DO1-DO2 of B2
A（DI5-DI6） B3（DO1-DO2） DI5-DI6 of A control DO1-DO2 of B3
A（DI7-DI8） B4（DO1-DO2） DI7-DI8 of A control DO1-DO2 of B4
A（DO1-DO2） B1（DI1-DI2） DO1-DO2 of A controlled by DI1-DI2 of B
A（DO3-DO4） B2（DI1-DI2） DO3-DO4 of A controlled by DI1-DI2 of B2
A（DO5-DO6） B3（DI1-DI2） DO5-DO6 of A controlled by DI1-DI2 of B3
A（DO7-DO8） B4（DI1-DI2） DO7-DO8 of A controlled by DI1-DI2 of B4
Notes: A and B should use the same channel
7. Contact: Sunny Web: www.lensen-tech.com Email: sunny@lensen-tech.com
4) One master to 8 salves
Remarks:
Master Salve Input and control correspondence
A（DI1） B1（DO1） DI1 of A control DO1 of B1
A（DI2） B2（DO1） DI2 of A control DO1 of B1
A（DI3） B3（DO1） DI3 of A control DO1 of B1
A（DI4） B4（DO1） DI4of A control DO1 of B1
A（DI5） B5（DO1） DI5 of A control DO1 of B1
A（DI6） B6（DO1） DI6 of A control DO1 of B1
A（DI7） B7（DO1） DI7 of A control DO1 of B1
A（DI8） B8（DO1） DI8 of A control DO1 of B1
A（DO1） B1（DI1） DO1 of A is controlled by DI1 of B1
A(DO8) B8(DI1) DO8 of A is controlled by DI1 of B8
Note: module A and B should use the same channel；
8. Contact: Sunny Web: www.lensen-tech.com Email: sunny@lensen-tech.com
5) To communicate with PC/PLC
6. Modbus RTU communication and value calculation
A. Register types：
Register types Read
command
Write command
(Control command)
Power off Function
coil register 0x01 Input --not support
Output-support
Not keep Can read input value
Hold register 0x03
1) Coil register:
Input: it is not convenient to read 8 I/O at one time. In order to control each I/O
correspondingly, we defined coil register. The register address is 0x0000-0x0007.
Output: it is not convenient to control 8 I/O at one time. In order to control each I/O
correspondingly, we defined coil register whose address is 0x0020-0x0028.
Note: The coil is not extra resource, just a method for register addressing---addressing by bit.
2) Holding register:
a. value of input channels: 0x0000
Note: addressing by bit means we only use the first 8 bits; bit0 to bit7 corresponds with I /
9. Contact: Sunny Web: www.lensen-tech.com Email: sunny@lensen-tech.com
O1 to I / O8;
b: feedback and value of output channels: 0x0040
Note: addressing by bit means bit 0 corresponds with 1/O1, bit 7 corresponds with 1/O8, bit
8-bit 15 corresponding value we write 0.
3) Reading method:
Coil register: read the responding register directly. 1 means OFF, 0 means ON.
Holding register: Can read all 8 inputs one time. We only use bit0-bit7 of this 16-bit register;
each bit represents the state of an input.
4) Control methods:
a. Use the coil--- write the corresponding coil register directly you can control its corresponding
output.
b. Use the holding register--- 8 outputs share 1 holding register, so you can manage each bit
separately to control the corresponding output.
B. About register control
IC Read/
write
Function
code
Products apply
Channel IC type PLC ID Modbus ID
DI1
Coil reg. 1 0 read 01
RDIO0202
RDIO0404
RDIO0808
Holding reg. 40001.0 0.0 read 03
DI2
Coil reg. 2 1 read 01
Holding reg. 40001.1 0.1 read 03
DI3
Coil reg. 3 2 read 01
RDIO0404
RDIO0808
Holding reg. 40001.2 0.2 read 03
DI4
Coil reg. 4 3 read 01
Holding reg. 40001.3 0.3 read 03
DI5
Coil reg. 5 4 read 01
RDIO0808
Holding reg. 40001.4 0.4 read 03
DI6
Coil reg. 6 5 read 01
Holding reg. 40001.5 0.5 read 03
DI7
Coil reg. 7 6 read 01
Holding reg. 40001.6 0.6 read 03
DI8
Coil reg. 8 7 read 01
Holding reg. 40001.7 0.7 read 03
DO1
Coil reg.
33 32 Read/
write
01/05
RDIO0202
RDIO0404
RDIO0808
Holding reg.
40065.0 64.0 Read/
write
03/06
DO2
Coil reg.
34 33 Read/
write
01/05
Holding reg.
40065.1 64.1 Read/
write
03/06
11. Contact: Sunny Web: www.lensen-tech.com Email: sunny@lensen-tech.com
7. DIP Setting：
1) Outer(right side) DIP 1-4 are used for channel setting. Please check the following definition:
Channel
No.
DIP Setting
Channel
No.
DIP Setting
Channel
No.
DIP setting
Channel
No.
DIP setting
1
1234
2
1234
3
1234
4
1234
5
1234
6
1234
7
1234
8
1234
2) Outer(right side) DIP 5-8 are used for mode setting. Please check the following definition
DIP Status Function Remarks
5
OFF Conventional modules
ON Customized modules
6、7 OFF、OFF 1 to 1 control Slave module’s ID set to 1
6、7 OFF、ON 1 to 2 slaves control 2 slave modules’ ID set to 1 and 2 respectively
6、7 ON、OFF 1 to 4 slaves control 4 salve modules’ ID set to 1, 2, 3 and 4 respectively
6、7 ON、ON 1 to 8 slave control 8 slave modules’ ID set to 1, 2, 3, 4, 5, 6, 7 and 8
respectively
8
OFF Slave mode
ON Master mode
12. Contact: Sunny Web: www.lensen-tech.com Email: sunny@lensen-tech.com
3) Interior(left side)DIP 1-8 are used for module’s ID setting (also Modbus slaves’ ID.), for
example
ID DIP setting ID DIP setting ID DIP setting
1 2 3
4 5 6
7 8 9
10 11 12
13 14 15
DIP for slaves with ID 16-254, please refer the above regulation to set
Note: Above picture of sample is ID1
8. How to connect LS-RDIO series Modbus RTU
13. Contact: Sunny Web: www.lensen-tech.com Email: sunny@lensen-tech.com
9. LS-RDIO series how to choose proper item：
Item No. Distance I/O numbers Power supply Interface
LS-RDIO 0202
600m、3000m
2DI、2DO
9-36V DC RS-485LS-RDIO 0404 4DI、4DO
LS-RDIO 0808 8DI、8DO
