Hong Kong – Drinking Water Safety Advisory Committee visits advanced water treatment facilities in Tai Po Water Treatment Works, Unmanned Surface Vessel System and Floating Solar Power System in Plover Cove Reservoir (with photos)

Drinking Water Safety Advisory Committee visits advanced water treatment facilities in Tai Po Water Treatment Works, Unmanned Surface Vessel System and Floating Solar Power System in Plover Cove Reservoir (with photos)


     The Drinking Water Safety Advisory Committee, accompanied by the Director of Water Supplies, Mr Tony Yau, visited the advanced water treatment facilities in Tai Po Water Treatment Works (TPWTW), the Unmanned Surface Vessel (USV) System and the Floating Solar Power (FSP) System in Plover Cove (PC) Reservoir this afternoon (October 17).

     The expansion of TPWTW was completed in 2018. The expansion not only increased the daily output capacity of TPWTW to meet the fresh water demand but also introduced several advanced water treatment technologies.

     The Advisory Committee first toured the Central Control Centre at TPWTW to learn about its operation, the advanced water treatment process and the Integrated Treatment Information & Tele-alert System which assists in water quality monitoring. 

     They then visited the ozone generation plant to know about the use of ozone as an advanced and efficient drinking water disinfection technology which can reduce the chlorine consumption by around 30 per cent. After that, they arrived at the first on-site chlorine generation facility in Hong Kong. The facility can generate the chlorine amount on demand and largely eliminate the risk of chlorine leakage during transportation and storage of liquid chlorine. The above facilities can help enhance the operational safety and flexibility of drinking water disinfection.

     They then visited the USV System for use in the water quality monitoring in PC Reservoir. Each USV is equipped with a Global Positioning System and an automatic water quality monitoring and sampling unit. It enables simultaneous monitoring of water quality at different locations in the reservoir and automatic generation of visualised water quality reports. The Water Supplies Department is currently enhancing the intelligence of the USV System to increase the efficiency of water quality monitoring, including intelligent route planning and intelligent water quality monitoring and sampling capabilities, by allowing the USV System to automatically plan cruise routes and respond in real time to changes in water quality.

     Lastly, they visited the FSP System in PC Reservoir. The pilot project can generate as much as 120 000 units (kilowatt-hours) of electricity annually, reducing around 84 tonnes of carbon dioxide emission. The pilot project will lay a solid foundation for a long-term development of renewable energy with some useful reference data for the future implementation of large-scale floating photovoltaic farms in Hong Kong.

DOCOMO’s AI system optimizes micromobility management, including vehicle reallocation and battery replacement

Enables inexperienced personnel to work efficiently and simplifies calculation of best logistics routes in new territories


NTT DOCOMO, INC. announced it has launched the Sharing Operation Optimization System, which uses AI to optimize operations for maintaining effective allocations of shared micromobility vehicles and replacing depleted batteries used by these vehicles.

The system was adopted by DOCOMO BIKE SHARE, INC., the provider of a bicycle-sharing service that allows users to reserve electric-assist bikes at the most convenient bike station, ride comfortably around the city, and then freely choose their preferred return station. Hereafter, the system will be gradually deployed throughout Tokyo to manage the companys shared-bike fleet.

Micromobility-sharing services allow users to easily rent bicycles and other small, lightweight vehicles and then conveniently return them to any station operated by the service. As the number of sharing-service users continues to grow globally, the corresponding increases in vehicles and renting/returning stations is making it difficult to ensure that vehicles in each fleet are optimally allocated and equipped with charged batteries at all times.

DOCOMOs new system uses AI to generate optimized plans for collecting and reallocating vehicles and replacing spent batteries. The system uses machine learning to simulate vehicle movements in order to predict the availability of vehicles and charged batteries at each station. Maintenance personnel can then use tablets or other mobile devices to view precisely which vehicles need to be trucked to other stations and which batteries need to be replaced for maximum operational efficiency.

The system forecasts rental/return trends based on a variety of data, such as in-use and returned vehicles, weather forecasts, date and time, travelling distances between stations, and each trucks storage capacity as well as quantities of vehicles and batteries on board any truck at any time. Using this information, the system generates an optimized reallocation plan, including the best transport routes. The system enables personnel with less experience to function as efficiently as experienced staffers. It is also expected to help operators to develop efficient operating routes in new territories.

Going forward, DOCOMO will continue to assess the systems performance, including its forecasting accuracy and the effectiveness of its recommended routes for bike reallocation, based on which the company expects to further upgrade the system and adapt the technology for supply-and-demand optimization in various other fields.

AppendixSystem Overview

DOCOMOs Sharing Operation Optimization System consists of three technologies: demand forecasting, simulations and reallocation planning.

1. Demand Forecasting Technology

Demand forecasting technology makes hourly predictions of how many vehicles will be in use and how many will be available at each station over the next 24 hours. In addition to real-time data, the system takes into account other data such as weather forecasts and dates and times. It has been shown to have the capacity to accurately forecast changing variations in vehicle demand.

2. Simulation Technology

This technology uses Multi-Agent Simulations*1 to project the precise movements among stations by the shared vehicles and their logistical-support trucks. The movement of each vehicle is forecasted using real-time data and statistics, including the probability of vehicles moving between specific stations and predicted demand at each station. Using these input values, the system forecasts the number of vehicles at each station and the remaining charge of each vehicles battery. Simulations are run every 10 minutes to enable the reallocation plan to be continuously updated.

3. Reallocation Planning Technology

Reallocation planning technology uses the simulation results to generate joint optimization plans*2 for vehicle collection/reallocation and battery replacement at each station, including the order in which trucks should visit stations and which transport routes to take. Forecasts of conditions in coming hours support the prioritization of battery replacements at the busiest stations, collections at stations where vehicle returns are expected to spike, etc., helping inexperienced personnel to work more efficiently and operators to develop efficient operating routes in new territories.

  1. Replicates the behavior of people interacting with others and their surrounding environments, such as when growing numbers of people avoid an increasingly crowded road, thereby changing the overall flow of people.
  2. Computational process to maximize or minimize evaluation values calculated from the combination of several different indicators by repeatedly performing appropriate actions on functions that are controllable by the system.


NTT DOCOMO, INC., Japans leading mobile operator with over 86 million subscriptions, is one of the worlds foremost contributors to 3G, 4G and 5G mobile network technologies. Beyond core communications services, DOCOMO is challenging new frontiers in collaboration with a growing number of entities (+d partners), creating exciting and convenient value-added services that change the way people live and work. Under a medium-term plan toward 2020 and beyond, DOCOMO is pioneering a leading-edge 5G network to facilitate innovative services that will amaze and inspire customers beyond their expectations.

Hong Kong – Planned system enhancement for the Faster Payment System on October 30, 2022

Planned system enhancement for the Faster Payment System on October 30, 2022


The following is issued on behalf of the Hong Kong Monetary Authority: 

     The Faster Payment System (FPS) service will not be available from 1am to 11am on October 30, 2022 (Sunday) due to a planned system enhancement, carried out by the Hong Kong Interbank Clearing Limited, the operator of the FPS. 

     The Hong Kong Monetary Authority has requested banks and stored value facility (SVF) operators to provide advance and timely notifications to their customers. Members of the public may contact individual banks or SVF operators for more details regarding the availability of the related FPS services that may be affected by the system enhancement. In case of need, members of the public may make advance arrangements for their payment activities.

Food-tracking AI system developed to reduce malnutrition in LTC homes

New technology automatically records and tracks how much food residents consume


New technology could help reduce malnutrition and improve overall health in long-term care homes by automatically recording and tracking how much food residents consume.

The smart system, developed by researchers at the University of Waterloo, the Schlegel-UW Research Institute for Aging and the University Health Network, uses artificial intelligence software to analyze photos of plates of food after residents have eaten.

The sophisticated software, which examines colour, depth, and other photo features, can estimate how much of each kind of food has been consumed and calculate its nutritional value.

“Right now, there is no way to tell whether a resident ate only their protein or only their carbohydrates,” said Kaylen Pfisterer, who co-led the research with her husband, Robert Amelard, while earning a PhD in systems design engineering at Waterloo.

“Our system is linked to recipes at the long-term care home and, using artificial intelligence, keeps track of how much of each food was eaten to make sure residents are meeting their specific nutrient requirements.”

It is estimated that more than half of residents of long-term care homes are either malnourished or at risk of malnutrition.

Food intake is now primarily monitored by staff who manually record estimates of consumption by looking at plates once residents have finished eating.

Amelard, a Waterloo alumnus and postdoctoral fellow at University Health Network, said studies show the subjectivity of that process results in an error rate of 50 per cent or more. By comparison, the automated system is accurate to within five per cent, “providing fine-grained information on consumption patterns.”

Researchers collaborated with personal support workers, dietitians and other long-term care workers to develop the system, which saves time as well as improves accuracy and would ideally be added to tablet computers already used by front-line staff to keep electronic records.

“My vision would be to monitor and leverage any changes in food intake trends as yellow or red flags for the health status of residents more generally and for monitoring infection control,” said Pfisterer, now a scientific associate at the University Health Network Centre for Global eHealth Innovation.

The research team also included Heather Keller, a professor of kinesiology and health sciences, Alexander Wong, a systems design engineering professor, and students Audrey Chung, Braeden Syrnyk and Alexander MacLean.

A paper on their work, Automated food intake tracking requires depth-refined semantic segmentation to rectify visual-volume discordance in long-term care homes, appears in the journal Scientific Reports.

Notification issued for Fire Alarm system and Fire protection system in Passenger Compartment in buses

The Ministry of Road Transport and Highways, vide notification dated 27th January 2022, has introduced the Fire Alarm System and Fire Protection System in the Passenger (or, Occupant) Compartment in buses through an amendment in the AIS (Automotive Industry Standard)-135 for Type III buses [‘Type III’ Vehicles are those designed and constructed for long distance passenger transport, for seated passengers ] and School Buses.

At present, fire detection, alarm and suppression systems are notified for fires originating from the engine compartment, as per AIS-135. Studies on fire incidents indicate that injuries to passengers are mainly due to heat and smoke in the passenger compartment. These injuries can be prevented if the heat and smoke in the passenger compartment is controlled by providing an additional evacuation time to  occupants by thermal management during fire incidents.

A water mist- based active fire protection system and  a standalone fire alarm system for buses has been designed to manage the temperature in the passenger compartment within 50 degrees centigrade.

This amendment to the Standard has been undertaken in consultation with  stakeholders and experts from the Centre for Fire Explosive and Environment Safety (CFEES), a DRDO establishment, working in the area of fire risk assessment, fire suppression technologies, modelling and simulation etc.

Click here to see GSR Fire Alarm System



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