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Ready-to-use software (win-x64)

Digitalisation has made key information such as sound/image/video training samples, geographic conectivity (Open Street Map), weather information, etc. widely available to the public. With advanced instrumentation, companies can collect real-time information such as machine condition instantly. To maximise the benefits of digitalisation and turn information into savings, our products are rich in features and span across fleet optimisation, assignment optimisation, anomaly detection and data analytics. Besides traditional machine learning, we have hybrid algorithms integrating deep learning with optimisation

Color: route of control forecast. Gray: route of ensemble forecast

Ship Route Optimisation

IMO resolution A.528(13) (International Maritime Organization, 1983) advises ships to make use of weather information for routing. Our software is a mathematical approach utilising AI weather forecast to minimise fuel consumption and CO2 emission. Ship operators can use engineering models to analyse performance and conduct what-if analysis without actual sailing. Its key features are as follows

Avoids unfavourable wind and wave to find paths with good fuel efficiency and emission for ensembles of weather forecast

Find alternative routes for difficult weather and fine-tunes departure time
Multi-faceted and consider factors like forbidden lat/lon due to geopolitical consideration

DIMS software

Fleet optimisation

Demand Integrated Maintenance Scheduling (DIMS)

Minimise fleet size for systems on preventive maintenance arising from demanded usage. Compute vehicle the spare ratio described in page 40 (Suggestions for further research) of 'Transit Cooperative Research Program 109 - System-Specific Spare Bus Ratios Update'

Assigning Systems On Round Trips (ASORT)

Minimise fleet size for systems on round trips, such as ferry, coach, etc.

Dynamic Shift Pattern Optimizer (DSPO)

Shift patterns of https://en.wikipedia.org/wiki/Shift_plan are efficient if workload remains the same for all shifts. Opportunity for saving arises when demands vary across shifts, for example, customer arrivals at 24-hour service centre are different for day versus night. For dynamic demands and rest requirements, DSPO computes efficient shift patterns using minimal staff size

Constrained clustering

Assignment optimisation

Constrained clustering

Assign points evenly to clusters with guarantee on attribute such as performance/similarity/distance. Applications are

Assignment of locations to autonomous clusters. In an efficient distribution system, supply points needs to be close to customers. Given a series of locations, our model constrains demand points such that each of them are within x minutes from a supply point. The resultant clusters can then be treated as separate distribution systems which operate autonomously
Assignment of members to teams. To facilitate group work, teams should not be composed of members with vastly different attributes. Our model hence constrains members in the same team to be within a specified proximity threshold

Constrained vehicle routing

Routing vehicles should be multifaceted and include practical constraints like forbidden zones, time-window, breaks, etc. Our software is able to use Open Street Map, and present result in vehicle view for quick analysis

Minimum vertex cover

Minimise fleet size required to cover a complex network. A simple example is to cover all hallways (edges) connecting all rooms (nodes) with security cameras. For complex problems such as analysis of travel connectivity of a geographic region, our software is able to use Open Street Map to create the required network from travel time/distance thresholds

Sound spectrogram

Data analytics

Abnormal sound detection

Use one-class support vector machine which requires only normal sound for training

Fair regression/classification modelling

Use representative sampling and kernel support vector machine to reduce class imbalance and bias

Explainable modelling

Build quadratic expansion models using quantile regression and support vector machine. Models are explainable, more accurate and require only hundreds of data points

Visualise neural network mapping

Global optimisation hybrid deep learning

Anomaly detection and classification of images

Use kernel support vector machine for better accuracy. In the scenario of detecting diseased plants, from past studies, about a few hunderd images of each class is adequate

Image regression

Build regression model from images for applications such as estimating food quantity, age from X-ray, etc. For X-ray age estimation, factors such as gender, weight, etc. are important and should included. Since gender is just a single field, merging with image which is high dimensional requires careful treatment. For high-dimension low-sample size data, support vector machine is known to suffer from data piling at the boundary, which can diminish generalisability. This means that new approaches are required

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Ship Route Optimisation

Fleet optimisation

Demand Integrated Maintenance Scheduling (DIMS)

Assigning Systems On Round Trips (ASORT)

Dynamic Shift Pattern Optimizer (DSPO)

Assignment optimisation

Constrained clustering

Constrained vehicle routing

Minimum vertex cover

Data analytics

Abnormal sound detection

Fair regression/classification modelling

Explainable modelling

Global optimisation hybrid deep learning

Anomaly detection and classification of images

Image regression