Tech Offers

Hierarchical Transactive Energy Management System

Hierarchical Transactive Energy Management System (HTEMS) is proposed not only to promote purchase/sale of energy transactions but also to solve the intermittency and variability of renewable energy sources (RESs) when they are integrated as part of the utility grid. A power hardware-in-the-loop (PHIL) platform is developed to test out the hardware controllers and converter design via the 4-quadrant three-phaser power amplifiers and to enhance the research capabilities for the smart grid technologies.

A low-cost transformer-less flexible power quality conditioner (FPQC) is invented to solve the power quality (PQ) problems of power systems due to the increasing usage of power converters. A series of multiport converters are developed to facilitate the integration of RESs into the power grid with the least number of components and acceptable cost.

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Innovative Dual Function Soil Moisturizer

Soils which are exposed to iterative wetting and drying in a tropical region, such as Singapore, are prone to develop hydrophobic coatings on the soil particles which results in hardness and water repellence. This condition limits the rate and capacity of water absorption, which leads to poor infiltration and water retention in soil. In these circumstances, water has tendency to either evaporate or run off instead of infiltrating into the soil, resulting in poor irrigation efficacy and water wastage. This problem can be further compounded by sloping terrain such as on mounds and on hillsides, where water is more likely to run off. These terrain will dry out quickly and requires additional irrigation, while the runoff collects in lower areas, creating wet spots that contribute to promoting soil compaction and encourage disease. Such phenomenon is detrimental to plant growth. A new formulation composed of surfactants and super-absorbent polymer dispersions, RetenSol, is createdto promote both soil wetting and moisture retention.The technology has been tested for a range of plant species and shown to improve the long-term water availability, serving as a mini water reservoir. During irrigation, the innovative formulation is able touptake large amount of water, and the locked water moleculeswould not easily evaporate and serve as a reservior that will release water during drought stress.

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Crowd Simulation

Crowd Simulation is an AI library that is equipped with a comprehensive set of tools to enable easy creations of group behavioural animations that are provided with flexible controls over the formation shape and movements of groups or flocks, which is extremely useful and valuable for rapid digital games development and simulation.

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Concept-Level Sentiment Analytics


Users do not need to change their OS, UI or IDE: our APIs are easy to use and to embed in any framework. Our company offer fine-grained solutions to many subtasks of sentiment analysis, e.g., polarity detection, aspect extraction, subjectivity detection, temporal tagging, named-entity recognition, concept extraction, personality recognition, and sarcasm detection, and they are available in different domains, modalities, and languages.


We show you what data is collected and how each of them is classified. Most companies, instead, adopt a black-box strategy in which they only show you the classification results. This way users can never be sure about how accurate their analysis really is because they usually do not disclose neither the data nor the techniques adopted for classifying such data (which, in most cases, are rather obsolete).


NLP research is evolving very fast, and the only way to be up-to-date with it is to be fully immersed in academia. We are not just a business company but also a research lab. We know the current and future trends of NLP, and we always embed the latest techniques in our APIs. Unlike most companies (which tend to focus only on one facet of the problem), we take a very multidisciplinary approach to sentiment analysis.

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Learning Analytics System for Step Based Learning Activities

Learning analytics systems collect, measure and report data about learners to support instructors to understand students’ learning and make informed interventions to optimize learning. Existing technologies mainly make use of log data collected from the learning environment and aggregate statistics such as no. of access to learning resources, learning activity attempts and performances. Those statistics will be used as features of students’ learning to derive learning patterns or train predictive models. While the features related to resource accesses and performances are insightful, the data that captures the process of how students solve the problems in learning activities could provide teachers more insight on students’ mastery of the knowledge and problem-solving strategies. In our learning analytics system for step-based learning activities, we collect, analyze and visualize students’ problem-solving steps and patterns. By correlating the analysis result with resource accesses and performance, teachers, as well as students themselves, can have a more informed intervention/reaction on how to improve learning and achieve learning outcome.

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Malay-English Machine Translation System

This is a tool to translate an English sentence into Malay and vice versa. Developing a translation tool for low-resource languages like Malay has always been a challenge. The main challenge comes from the fact that machine translation systems typically rely on a huge amount of sentence-parallel data, and creating such datasets is an expensive process. In our work, we collected parallel datasets from various sources including News, OpenSubtitiles (OPUS), etc. Therefore, our corpus is quite generic and covers both texts and conversations. The second challenge is to train a Machine Learning model. Neural Machine Translation (NMT) is a recently proposed deep learning architecture that has quickly become the standard approach. It offers an end-to-end architecture with better generalization. In the last few years, researchers have proposed many techniques to improve NMT, including work on handling rare words and using attention mechanisms to align input and output words. Our translation system utilizes the most up-to-date NMT architecture, namely the transformer net and the seq2seq architecture. To train our model we used OpenNMT-py framework, which is a standard in the MT community for its robust and modular implementation.

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