TECH OFFERS

Manufacturing plants constantly seek opportunities to save energy, reduce cost, and be more environmentally sustainable. However, achieving these goals often requires heavy expenditure in the form of hiring teams of experienced engineers, who then perform cost-reduction tasks manually - this method is time-consuming, costly, and prone to inaccuracies due to the risk of human error.  This technology offer provides a no-code Artificial Intelligence (AI) powered platform that monitors energy consumption, carbon dioxide(CO2) emission, and operational expenditures (OPEX) in real-time. The AI engine builds a virtual cognitive model (digital twin) of a physical asset, e.g. a manufacturing plant or a piece of machinary. Simulations are carried out on the model to predict operational inefficiency i.e. high energy usage, equipment breakdown, etc. Upon detection of inefficiencies, the engine is able to suggest the best operating parameters to resolve the inefficiency.

This technology offer is technique that can turn renewable cellulose paper feedstock into lightweight carbon foams that are architected into highly complex geometries that cannot be produced through traditional manufacturing techniques, such as closed-cell lattices. These carbon foam lattices exhibited excellent mechanical properties, particularly in energy absorption, as well as good battery characteristics, low thermal conductivity and relatively good electrical conductivity. Unlike most traditional carbon foams that are brittle, paper-based carbon foams can withstand ~ 30% strain before significant deformation sets in. These multifunctional properties, the quick and easy customization of part geometry and the use of green feedstock are expected to be useful for aerospace, automobile, sports, medical and thermal insulation markets, as they search for the next generation of eco-friendly, high-performance materials. This technology is available for R&D collaboration, IP licensing, or test bedding, with partners such as battery manufacturers, supplier to battery manufacturer, space industry, etc.

Existing methods for load monitoring typically focus primarily on residential building data, while few look at the effectiveness of such systems for industrial or commercial buildings. Apart from the use of this technology for real-time supply-demand response, such methods can be extended for use in anomaly detection, small-scale load change detection, or an estimation of energy usage, without the associated high costs of sub-metering equipment. The proliferation of neural networks for such demanding tasks solves the computationally expensive problem of traditional methods like Hidden Markov Models (HMM) and fuzzy clustering algorithms. This technology offer is a neural network solution for residential and industrial energy management. It utilises a time-series forecasting tool to predict load, renewable energy generation, and electricity prices, without the need for costly sub-metering equipment. It is based on reinforcement learning algorithms which are trained by rewarding and penalising neural network algorithms for good or bad decisions respectively, the solution is a non-intrusive technique that helps residential and commercial end-users save on energy costs in the open energy market by scheduling their load demand for heating, ventilation, air conditioning (HVAC) systems, washing machines, and charging of their Electric Vehicles (EVs).

Machine Learning (ML) and Deep Learning (DL) have been the primary growth driver of Artificial Intelligence (AI) and has seen widespread adoption in areas such as Computer Vision, Speech Processing, Natural Language Processing, and Graph Search, among many others. It is also well-known that AI both needs and produces large amounts of data. However, traditional data repositories have not scaled effectively to handle the large amounts of vector representations that are common in AI applications - in such cases, searching for similarities across high-dimensional vectors is inefficient. To address such limitations, vector databases have been developed to address the limitations of traditional hash-based searches and search scalability, enabling similarity searches across large datasets. This technology offer is a unified Online Analytical Processing (OLAP) data platform that supports approximate vector search, enabling efficient searching over billion-scale structured data and vector data. The data engine simplifies the process of building enterprise-level AI applications such as search and recommendation systems, video analytics, text-based searches, and chatbots while accelerating the development of production-ready systems. Developers no longer need to deal with complicated scripts to query vector data as low latency, high-performance structured data, and vector data searches are made possible via vector data indexing methods and the use of extended Structured Query Language (SQL) syntax.

Different plant species have different nutrient requirements. The current practice of urban farming uses a generic hydroponic nutrient solution that is suitable to most plant types, and a crude sensing system that only measures total ion content in the solution. This approach often results in nutrients deficiency and/or overloading and hence requires consistently monitoring. Overloading of nutrients not only increases the input costs, it also results in greater quantities of contamination in effluent to be disposed after harvest.  A targeted hydroponic nutrient solution reduces the need to periodically adjust the nutrient. The technology provider has studied and formulated different nutrient recipes that had shown improved yield compared to commercial products. This ensures the best growth for each crop type. It also reduces common problem stemming from imbalanced nutrient, e.g. leaf chlorosis due to nutrient deficiency. All these translate to a better yield and a more marketable produce for the farmers. Formulations developed include Mizuna, Kale, Lettuce, Mustard, Kalian, and Caixin. The technology provider is seeking for licensing partners from the agriculture industry.

Tile grouting is the process of filling up gaps between tiles, after individual tiles have been laid onto cement screed and is a critical part of virtually every construction project. Yet, it remains a highly laborious process, and is considered one of the most physically demanding tasks as it often results in injuries to tilers' knees and back. This, in turn, leads to quality issues when grouting is not performed correctly. The construction labour shortage in Singapore, especially in the tiling/construction industry has likewise catalysed the demand for automation of such jobs - especially since such tasks are deemed to be less desirable to a younger generation of workers. This technology offer is a tile grouting robot powered by Computer Vision (CV) and Simultaneous Localisation and Mapping (SLAM) techniques, running on Robot Operating System (ROS2) to boost construction productivity and reduce the occurrence of workplace injury. The robot is able to boost productivity by at least 5 times and this results in an amortisation time of roughly 24 months.

The presence of heavy metals in food or feed powders involves contamination of the food chain and potential harm to public health, as such, rapid detection is a time-critical issue. The uncertainty about food safety caused by the possible presence of heavy metals is of concern to consumers and regulatory authorities and this is typically addressed by increasing the testing frequency of food or feed samples. However, existing testing methods are often time-consuming and require highly skilled laboratory personnel to perform the testing. This technology employs spectroscopic imaging methods and machine learning techiniques to rapidly detect heavy metals in food or feed samples. The machine learning model can perform a multi-class differentiation of the various heavy metals based on spectroscopic measurements. It is also able to predict the concentration of heavy metals present in food or feed powders using spectroscopic measurements. Minimal sample preparation is required for this method, allowing for the rapid screening of food or feed powder samples. The technology owner is interested in collaboration with companies working with food powders, with an interest in heavy metal content within food powders.   

This technology offer is a Ultrahigh Frequency (UHF) Radio-Frequency Identification (RFID) tag antenna for use on metal structures. 2 versions are available: A compact dual-band version with folded strip structure, with a total size of only 20 mm × 30 mm × 1.5 mm. This tag can be well used in different RFID systems, which work at different UHF bands, such as European and American frequencies. The reading patterns of this tag are with different directions in two bands. A single band version with a total size of only 10 mm × 30 mm × 1.5 mm. This tag can be well used in planar as well as conformal platforms, such as metallic cylinders and bearings. Automated factories should be interested in these tags, and they can use the miniaturized tags with RFID technology to intelligently detect whether the machinery and equipment are running normally.

This technology offer is an integrated, CMOS-compatible, compact device that provides dispersion compensation of dispersion in optical fibers. Dispersion impairments is a well-known problem in the transmission of high-speed data over fiber, that limits both the fiber reaches, or poses lower limits on the power required. The technology developed allows a seamless, very low loss method for compensation of fiber dispersion, providing high magnitudes of dispersion for countering dispersion in optical fibers. Without dispersion compensation, signals are susceptible to degradation from optical fiber dispersion, with the extent of degradation worsening with longer fiber reaches. Without proper dispersion compensation, transmitted data will experience high Bit Error Rates (BER) at the receiver. This technology solves this important problem and increases the fiber reaches which may be served.