This is a microprocessor board for managing the battery pack recharge cycle for industrial machinery (handling machines).
The board controls and manages the charging of the battery pack according to the standard charging profile for lead-acid batteries.


Although very small in size, this board is capable of controlling peak currents of up to 100 A, with very little heat dissipation in relation to the energy transferred. It is an economy and “energy-saver” board.

The board “manages” the charging of the battery, so the “power interface” with the mains (usually just transformer + rectifier bridge) is separate.
The card current sensor is based on the use of a “brass plate” in series with the negative battery conductor, making wiring in the system simple and economical.

The card monitors the battery status during charging. In the event of anomalies, it stops recharging and warns the operator by means of LEDs.

Again with a view to energy-saving, at the end of the recharge cycle, if correctly wired, the card is able to disconnect the “power interface” from the mains. This eliminates unnecessary energy consumption from the mains, as in the case of long pauses between charging cycles.

Particular care has been taken in the management of recharging so as to achieve “clean” switching, thus maximum reduction of electromagnetic interference [EMI] and a strong reduction in noise from the power interface.
The board, which is produced in various models, allows the requirements of mains voltage as well as various battery packs to be met.
The power supply is taken directly from the battery pack to be recharged.

The simplicity of the necessary parts, reduced to a minimum, and careful attention to the technological choices made, make this board a truly economical and easy-to-use solution for battery recharging.


  • Mains voltage: 110V; 230V nominal
  • Manages battery packs of: 12V,24V,36V,48V
  • Nominal Battery-pack current sensor: 1 SHUNT
  • Battery-pack voltage sensors: 1


  • Cargo handling
  • Manufacturers/maintenance of forklifts
  • Battery-powered machines for cargo handling
  • Marine sector



Ultrasound power generator board for 1MHz and 3MHz handpieces. Product typically used in the “AESTHETIC” sector.


he board generates the power signal for driving 1MHz and 3MHz PIEZOCERAMIC handpieces [alternatively] with continuous modulation of the power supplied by amplitude modulation.
It implements sensors for

  • disconnected
  • short-circuited
  • generic overload

It has a connector for connecting 2 external LEDs indicating the active handpiece.
A sophisticated JOB sensor [optional] allows effective ultrasound delivery only when the handpiece touches the skin to be treated and as long as it remains in contact.
The delivery mode can be continuous or modulated ON/FF with all possible parameters programmable by the user.
The communication port used is the typical RS232 serial port [therefore connectable to a PC].
A sophisticated hardware + firmware system allows the handpiece drive signals to be generated digitally, i.e. expressed in numbers.

A special function [SEARCH], allows to find the specific resonance frequency of the mounted handpiece by a simple use of the PC. It is possible to save on the local PC all the specific operating parameters of the handpiece, then back up the data, or to attach the file to the handpiece to a remote customer, who by connecting the handpiece and uploading the file on the board, gets back the same operation as before; this feature can be used to supply “already calibrated” handpiece-replacements.
A large number of programmable parameters make it possible to customise/customise operation according to the most diverse requirements. In addition, the numerous operating ‘sensors’ allow you to work safely and have real time diagnostics on everything significant.
It handles a large number of control/alarm functions in current use and the diagnostics are therefore very accurate.


  • Board power supply: 12Vdc / 0.5A
  • Nominal Handpiece power supply: 30Vdc / 2.5A nominal
  • Handpieces manageable: 2, 1MHz & 3MHz alternatively
  • Control ports: 1 [RS232].


  • Beauty equipment
  • Cream manufacturers



Ultrasonic generator board at 30KHz power with remote controlled emission.

Typical product in the ” AESTHETIC ” sector.


Power generator module controlled remotely via a communication port.
It implements all user and diagnostic-user functions, such as actuator, controlled by a superior system such as PLC or other control system.

It supports PIEZOCERAMIC handpieces at 30KHz or similar frequencies, as it is possible to ‘calibrate’ the specific handpiece directly on the board.


  • Card power supply: 12Vdc/250mA
  • Handpiece power supply: 48Vdc/1.5A
  • Handpieces managed: 1 PIEZO 30KHz
  • Control ports: 1 specification for “HUB” management


  • Manufacturers of beauty equipment
  • Solvent-based washing machines without drums



Mining has become an increasingly precise practice. Modern mines have applications that ship and synthesize data from a variety of sources resulting in accurate mine planning, scheduling, operations and transportation. Data flows in from diggers, grinders, concentrators and trucks. In conjunction with data from enterprise systems, miners are able to make sophisticated forecasts. They know that a pump will fail in exactly 3 months, a truck’s bearings will need replacement in 2 weeks or an electric rope excavator will stop.

Cognitive computing and predictive analytics together with machine learning technologies are poised to heighten the levels of accuracy and preciseness in operational areas. These are the technologies to put on your must-watch list.

Let’s briefly consider a scenario which mining experts suspect leads to inefficiencies and bottlenecks, but unfortunately have little or no control over. This scenario concerns the operators of functioning within the next 5 shifts.  It is relatively easy to continue to make improvements around the edges of available data and reap incremental benefits. However, with the technology available today, it is possible to stretch the horizons of intelligent mining beyond the existing paradigm. As an example, cognitive computing and predictive technology can accurately tell us where to dig, how much to dig and where the ore should be shipped for blending in order to meet contracts or take advantage of a sudden spike in prices.

Big excavator equipment

These are large and expensive machines – so expensive that mining operations cannot afford to have standby excavators. They need to operate at maximum efficiency with minimal downtime. While machine performance data can predict when an excavator is likely to fail, there is no accurate way to measure poor operator performance during a given shift and neutralize it so that production is not affected.

It is entirely possible that an operator is in a portion of the mine where the geology is highly variable and the excavator is loading the incorrect material onto a truck. This is not necessarily a fault of the operator. It is a professional hazard. Is there a way to alert the operator inside the cab and direct the operator’s attention to the correct material? In other words, can we augment the operator’s skill, experience, knowledge and judgment and dial up the levels of performance to absolute maximum?

A peek into the future

  • As it turns out, we can. A modern mine operation that uses an Artificial Intelligence platform that can be ‘plugged in” to its total environment can provide digital assistance to the operator. The system inside the cab could listen to the operator using a natural speech algorithm and maps this against actions of the excavator. The system would learn with every action and iteration. The system would also use the vast array of sensors on the equipment to provide real-time feedback and guidance to the operator so that they can adjust their actions and get better performance. 

    Geological information could be displayed on the cab window as a HUD or a Heads-Up-Display (see Figure 1: Augmenting Operator Skills, Experience and Judgement Using Cognitive and Machine Learning Technology). The simple-to-follow graphic representation of the mine ensures the operator knows where to dig for maximum efficiency.

Learnings from other industries

  • Now let’s move this scenario up by one level. Imagine for a moment that the cognitive system in the cab has access to operational information about the excavator. The system is capable of monitoring and mapping, say, the swing of the excavator arm against optimal metrics. The system automatically alerts the operator every time there is an over-swing, informing the operator of the exact loss in productivity, “Harry, that continual over-swing cost us 3% in productivity last shift. Need a visualization to control the arm accurately for the next pass?” Harry, our operator, can see (on the HUD) that he is falling behind on his shift objectives and immediately takes corrective action prescribed by the advanced on-board system.

    From here it is a simple step to gamifying the cab and delivering motivational data to Harry. A dashboard inside the cab can potential display not just Harry’s target productivity, but that of other operators in the shift (“Harry, John in Digger 17 in the East End of the pit is ahead by 10 points”). The leaderboard can inject some fun and games into achieving improved performance and, more importantly, motivate operators to enhance their skills using technology as a coach.

    At JOULEHUB we have been leading the way with cognitive computing, predictive analytics and machine learning across a variety of industries ranging from financial services to oil & gas and for horizontal processes such as help desk support. These are labor-intensive industries and practices where the pressure for urgent intervention to bump up the accuracy of processes and boost productivity is high. We believe there are several learnings from these industries that can be applied to mining:

    • Knowledge extraction from large structured and unstructured data sets and building industry and processspecific curated knowledge systems
    • Integrating multiple heterogeneous data sources to synthesize ideas or patterns and map them against industry models
    • Dynamically linking external and internal knowledge sources for real-time decision making
    • Creating human-centric visualization and seamless natural language machine interaction
    • Analyzing operator/ FTE behavior with visual and voice interfaces to create appropriate interventions
    • Modifying business processes with new information, users or interaction modes

    Cognitive computing and predictive analytics are going to lead the future of mining. These technologies will not only address productivity, but elevate human, asset and environmental safety to previously unattainable levels. At the moment, the logic and reasons to assess these technologies is exciting and spells a considerably improved future.