Monday, July 15, 2024

Map shows where Chinese ships spotted off U.S. Coast

see video
 
From Newsweek by Ryan Chan

The United States Coast Guard spotted four Chinese naval ships near an archipelago in Alaska over the weekend, at least the fourth encounter between the two sides near the "Last Frontier" state in recent years.

China's military ships were sailing in the Bering Sea on Saturday and Sunday, north of the Amchitka Pass and the Amukta Pass of the Aleutian Islands, according to the Coast Guard's statement, which did not identify the types of Chinese naval vessels it detected.

The islands lie between the south of Bering Sea and the north of Pacific Ocean.
The Amchitka Pass is a 50-mile wide strait while the Amukta Pass is 43 miles wide.
The Bering Sea is the doorway to the strategic Arctic region, separating Russia's Far East and Alaska.

China's vessels were sailing in international waters but within the U.S. exclusive economic zone (EEZ), the Coast Guard said.
They responded to radio communications and said they were conducting freedom of navigation operations (FONOPs), according to the agency.

The U.S. EEZ extends 200 nautical miles offshore and is the largest in the world, according to the National Oceanic and Atmospheric Administration, spanning over 13,000 miles of coastline and containing 3.4 million square nautical miles of ocean.
This zone extends beyond the seaward boundary of the 12-nautical mile territorial sea.

The 1982 United Nations Convention on the Law of the Sea grants a coastal state the sovereign right to exploit natural resources within its EEZ, and it shall have due regard to the rights and duties of other states.

During a routine maritime patrol in the Bering Sea and Arctic region, the U.S. Coast Guard Cutter Bertholf spotted and established radio contact with a Chinese People’s Liberation Army Navy task force in international waters within the U.S.
exclusive economic zone on August 30, 2021
Ensign Bridget Boyle/U.S.Coast Guard

The U.S Navy routinely conducts FONOPs in waters near China, including in the contested South China Sea.
In a post on X (formerly Twitter), Tom Shugart, a defense analyst and former Navy submariner, noted differences between the two countries' FONOPs.

"U.S. FONOPs are conducted to challenge excessive maritime claims made contrary to international law," he wrote.
But the U.S.—unlike China in the South China Sea—does not restrict operations within its EEZ.

U.S.Coast Guard cutter Kimball was tasked with shadowing the Chinese ships until they departed waters around the Aleutian Islands and transited into the North Pacific Ocean.

The U.S.Coast Guard cutter Healy, bottom, steams alongside the cutter Kimball, top, near Unimak Pass in Alaska on July 3.
Healy, a polar icebreaker, and Kimball, a national security cutter, patrol the waters around
U.S. Coast Guard/Chief Warrant Officer Brian Williams

A Coast Guard photo released by the U.S.Defense Department showed the Kimball operating alongside the polar icebreaker Healy on July 3 near the Unimak Pass in the Aleutian Islands.

Both ships "patrol the waters around Alaska to maintain maritime safety, security, and stability in the region," the Coast Guard said.

The Kimball is a multi-mission national security cutter operating from its homeport in Honolulu, Hawaii, according to the Coast Guard.
It has a displacement of 4,500 tons and a range of 13,000 nautical miles—and is equipped with automated weapons systems.

A Chinese military observer on X noted that, based on bulletins released by the Japanese Defense Ministry's Joint Staff Office, four Chinese naval ships transited near northern Japan and entered the North Pacific Ocean from June 30 to July 1.

It was not immediately clear whether they were the same ships spotted by the Coast Guard in the Bering Sea.

China's Defense Ministry did not immediately respond to a Newsweek email seeking comment.

It was not the first time China had sent an armada to the Alaska coast.
In the third encounter last August, 11 Russian and Chinese ships sailed close to the Aleutian Islands, where they were monitored by four U.S. destroyers and maritime patrol aircraft.
 
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Saturday, July 13, 2024

Jellyfish beauty : Chirodectes Maculatus & Bald Cyanea


Bald Cyanea is the best model for jellyfish anatomy

Friday, July 12, 2024

Algorithms in the Arctic – removing bad weather from images to make Arctic shipping safer

Researchers collect ice samples, while colleagues on board the research ship Kronprins Haakon keep watch for polar bears.
Photo: Daniel Albert, SINTEF
From Norwegian SciTech News by Solvi Normannsen

Arctic shipping traffic is on the increase.
One day, these ships will be autonomous.
New technology that can remove rain, snow and fog from the images produced by the ship’s cameras and sensors will increase safety in extreme conditions.
 

Imagine an autonomous ship sailing through one of the world’s most extreme ocean areas.
Sea ice is everywhere.
Fog, snow or rain make visibility extremely poor.
Just like ship captains see through their eyes, autonomous navigation algorithms perceive the world through sensors, and bad weather is just as impenetrable for sensors as it is for sea captains.

Getting rid of poor visibility

With the rise of Arctic shipping, something that can remove the bad weather from the images so the algorithms can see the surroundings as if it were a clear, sunny day could be extremely useful.
Now, PhD candidate Nabil Panchi at NTNU’s Department of Marine Technology has developed an algorithm that can do just that.

“We have put in place a new piece of the big puzzle for better modeling of sea ice,” Panchi said.

Behind the clouds, the sky is always blue, and on the other side of bad weather, there is always a clear view. Nabil Panchi adjusts the cameras on board the research ship Kronprins Haakon. Photo: Daniel Albert, SINTEF.
 
Current AI algorithms work well on clear images, but they struggle when images become blurry or degraded due to bad weather.

Panchi, who is also a naval architect, has used thousands of images from the Arctic to train the new algorithm so it filters out visual impediments such as rain, snow, and fog, as well as water droplets on the lenses of the cameras that many vessels are equipped with.

Panchi is affiliated with the DigitalSeaIce project, which is focused on multi-scale integration and digitalization of Arctic sea ice observations and prediction models.
The main objective is to build a digital infrastructure that integrates regional sea ice forecasting models and local ice-related models with shipboard and satellite-based Arctic sea ice and environmental observations.

Understanding the environment via images


“Our work is about understanding the Arctic environment through the use of images.
We are creating algorithms that work in all weather conditions” says Panchi.


Overview of the scientific activities aboard Norwegian research vessel Kronprins Haakon, as part of the second GoNorth expedition during which scientists discovered a new hydrothermal field between Svalbard and Greenland.
 
His research is based on thousands of images taken on a voyage with the research ship Kronprins Haakon in the Arctic during the summer of 2023.

In collaboration with his academic supervisor, Associate Professor Ekaterina Kim, he recently published the article ‘Deep Learning Strategies for Analysis of Weather-Degraded Optical Sea Ice Images’ in the IEEE Sensors Journal.

Panchi and Kim are introducing two ways of helping ships travel more safely in bad weather in the Arctic, by “removing” the weather from images.
One uses artificial intelligence to clean up the images, so that existing algorithms work as they should.
A slightly more efficient way is to develop new algorithms that work during bad weather.

“Both strategies allow us to understand the Arctic in all weather conditions,” Nabil says.

Cleaned images already in use in cities

Algorithms that can remove weather from images have been in use for a long time, but primarily in urban areas.
They are used to develop autonomous cars, and in security and camera surveillance.

Current algorithms that analyze sea ice are largely based on images taken from ships in good weather conditions.
The problem is that images from the Arctic are often unclear due to the fog, rain, and snow that are common weather conditions in these waters.
These types of images are poor material for the existing algorithms that are designed to understand the Arctic environment.

The algorithms also need to be trained to analyze the type of ice surrounding the ship, so they can indicate where it is safe to break through the ice, and which areas the ship should avoid.

The first open-access dataset of sea ice images


In order to remove fog and raindrops, algorithms must be trained to clean up weather-affected sea ice images.
“This area of research had largely been ignored so far.
The problem has been limited access to clear images from the Arctic – until now.
We hope that our new open-access dataset helps in future development of weather resilient technology,” Panchi says.

Panchi’s supervisor Ekaterina Kim has worked extensively in the Arctic, and in recent years she has been exploring how AI can be adopted to solve some of the challenges that exist in polar regions.

The two NTNU researchers have now made the SeaIceWeather dataset publicly available online.
It contains thousands of images and is the first open-access data set for sea ice.

Rain on one, clear weather on the other. When fed with a weather image, the AI model removes the raindrops and produces a much clearer image of the ship’s surroundings.
 
Facilitating safer voyages

“There are very few open-access datasets of this type.
A huge amount of work goes into making them.
We hope they are used as much as possible,” says Panchi.

Rain on one, clear weather on the other.
When fed with a weather image, the AI model removes the raindrops and produces a much clearer image of the ship’s surroundings.

Each picture comes in two versions: a ‘clean’ version with a clear view, and an unclear one due to weather conditions.
NTNU researchers hope that the SeaIceWeather dataset will be used by more people and that it inspires them to collect these types of images.

Many of the users are researchers who are working on sea ice and navigation models, or dynamic positioning.
These systems must work in all weather conditions, and the more images the algorithms are given to learn from, the more accurate the monitoring, ice warnings, and navigation will become – something which is very much in demand.

An AI-based system for sea ice analysis helps the crew understand the ship’s surroundings.
“We can use this information to develop advanced systems to avoid collisions, for safer navigation and the best sailing routes possible.
The latter will also help reduce emissions,” says PhD candidate Nabil Panchi (Illustration: Nabil Panchi)

More ships – and inexperienced captains


Global warming is causing sea ice to melt, increasing the amount of Arctic shipping.
More and more shipping companies are choosing these new routes that have now become ice-free.
Between 2013 and 2019, ship traffic in the Arctic increased by 25 percent.

“It takes a lot of experience to navigate safely through sea ice.
There are probably more ships in polar waters now than there are experienced sea ice captains.
The system we have built can provide better assistance for people maneuvering the ships,” says Panchi.

Arctic sea ice has become thinner, cracks more easily, and can make massive ice ridges or hummocks.
From the bridge of a ship, only one meter of ice might be visible sticking up above the surface, but not the 4-5 meters hidden below.
The likelihood of dents and hull damage is high, and not all ships are built to break through ice.

An AI-based system for sea ice analysis helps the crew understand the ship’s surroundings.
“We can use this information to develop advanced systems to avoid collisions, for safer navigation and the best sailing routes possible.
The latter will also help reduce emissions,” says PhD candidate Nabil Panchi


At the same time, autonomous shipping holds the potential to revolutionize the shipping industry – making it more efficient and safer.
According to Fortune Business Insights, the global autonomous ships market size is projected to grow from $6.11 billion in 2024 to $12.25 billion by 2032.

“We expect more autonomous technology on ships navigating through ice, and current systems need to be trustworthy in the extreme Arctic environment,” says Kim.

30 days of data capture

Panchi has trained the algorithms on two image datasets: one collected during the GoNorth voyage on Kronprins Haakon, the other obtained from online image databases.

He mounted two cameras on one side of the ship, with one camera directly under the other one.
The upper camera had a clear view, while they had mounted a transparent screen in front of the lower camera, which was sprayed with water to simulate raindrops on the lens.

The data collection set-up on board the research ship Kronprins Haakon in the Arctic.
The upper camera takes clear pictures, lcean, and the lower camera takes pictures with raindrops in front of the lens.
Illustration: Nabil Panchi. Background image: Daniel Albert, SINTEF/GoNorth.
 
In the observation room on the ninth deck, Panchi’s computer continuously downloaded the images of sea ice.
For 30 days, he collected thousands of pairs of images, each of which consisted of one clear image and one covered with artificial rain.

Training algorithms

In total, the datasets consist of over 4600 clear images, most of them from the research voyage.
Using algorithms, they created seven weather variants for each clear image: small, medium and large snowflakes, rainy weather, fog, and real and simulated raindrops on the camera lens.

Based on these variants, they then created the two datasets.
One of them presents images that indicate what kind of ice is located around the ship.
The other dataset divides the ice into different categories, such as ice floes, pancake ice, ice slush, drift ice, etc.

Three different image-cleaning algorithms were trained on the datasets, and when the researchers compared the results with the clear images, they could easily tell which algorithms were most accurate in relation to the different types of weather.

Only daylight and three types of weather

The method is limited in that all the images are taken in broad daylight and involve only three types of weather conditions.
Panchi points out that since the Arctic is in darkness from September to March, similar images should ideally be collected during the winter.
However, it is also fully possible to use Augmented Reality (AR) and create an artificial winter or night-time version of the existing images.

“So far, it’s mainly researchers who can use what we have done, but we hope that more people will use it in the future.
There are many factors affecting when this will actually happen; it may take up to 5 years before the models can be used commercially.
They must then be of a quality that make them a fully reliable assistant for ship management,” Nabil said.

Reduced emissions

The largest ships use enormous amounts of fuel.
They sometimes have to sail back and forth into the ice in order to break through, which requires a lot of energy.

“If you fully understand the conditions surrounding the boat, you or AI can plan the route and save time, effort and therefore emissions.
It will also make shipping safer.
There are a growing number of tankers carrying liquefied natural gas and other cargo sailing through the Arctic.
So far, there haven’t been any oil spill incidents, but if one were to occur, it would have serious consequences,” says Panchi.

A lot of unused image data

Monitoring polar waters is also important with regard to climate change.
Many ships have cameras and sensors that monitor their course.
There are lots of ships producing images, but hardly any of these images are available online.
According to Panchi, most of the images end up in maritime data archives, and – apart from a few insurance cases – are never used.


On the research voyage. PhD candidate Nabil Panchi (right) adjusts the camera, while PhD candidate Alexandra Pliss sprays water droplets on the screen in front of one of the camera lenses. Photo: Daniel Albert, SINTEF.
 
 “We see great opportunities in extracting useful data from these images. One of our goals is to develop algorithms that can be improved in real time, on site. Improving how we monitor Arctic waters will benefit society. It will provide a better basis for forming policies, and for sustainable and safe use of Arctic waters,” Panchi said.

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Thursday, July 11, 2024

NTSB: ATB grounding caused by failure to spot a charted ock

 
From MartimeExecutive

The National Transportation Safety Board has released its report on the grounding of an ATB cargo barge on a rock just off Kodiak Island last year.
The casualty was caused by improper use of an electronic chart system (ECS), according to NTSB: the master overlooked the symbol for a submerged rock, and the chart system's auto-checking function was not used to verify the safety of the voyage. 

 Area where the Cingluku/Jungjuk grounded, as indicated by a circled X.
(Background source: Google Maps)
 
On the morning of May 22, 2023, the ATB tug/barge combination Cingluku / Jungjuk left the small port of Togiak, Alaska, headed for Seward.
The Cingluku had supplies on board for another vessel, and planned to rendezvous in Shakmanof Cove on Kodiak Island.
The barge was unladen and partially ballasted, and was drawing about six feet of water at the stern.

 Approximate voyage trackline of the Cingluku/Jungjuk.
(Background source: Google Earth)
 
On May 23 at midday, as Cingluku transited False Pass, the captain - a 20-year veteran of the towing industry - laid out a course to Shakmanof Cove using the tug's electronic chart system.
It was his first time on a voyage into the remote bay on Kodiak's northern coast.
The tug continued on through the rest of the day and all of May 24, arriving off Kodiak on the morning of the 25th.
 
Track of the Cingluku/Jungjuk as it approached Shakmanof Cove, overlaid on
NOAA ENC US4AK5PM.
(Background source: NOAA ENC as viewed on Made Smart automatic identification system)
The United States Coast Pilot 9 (Alaska) noted a rock in the area where the ATB
grounded stating, “Kizhuyak Point: A rock, which uncovers about 4 feet, is 400 yards
north from this point. Shoal water extends 300 yards north of the rock.”

The tug approached Shakmanof Cove at about 1030 hours in the morning, and the mate joined the master on watch.
Two deckhands and the engineer were stationed on the bow in preparation for landing, but had little forward visibility because of the barge's large bow ramp.

At about 1047, as the ATB rounded Kizhuyak Point, the barge ran aground on a submerged rock about 400 yards off the shoreline.
The tug was not affected, and its ATB coupling to the barge remained in place. Alaska's strong tides came to the rescue and lifted the barge off about six hours later.
The crew remained in the cove to await good weather and arrange for a dive inspection.
Divers found no signs of penetration of the hull, and there was no flooding in the barge's void spaces.

After the ATB reached Seward, it was drydocked for a class inspection.
The damage included a 16-20 foot long dents in the bottom plating and frame damage in the same area, in way of the centerline forward ballast tank.
The total estimated cost of repairs came to about $1.5 million.

Hull damage along the bottom of the barge (NTSB)


Frame damage inside the ballast tank (NTSB)

While the rock was not visible from the surface, it was a charted hazard, and it appeared on the NOAA chart that the master had used to prepare the voyage plan.

ENC US4AK5PM, as viewed by investigators using an equivalent ECS.
The chart shows an isolated danger symbol over the rock that the ATB struck.
(Backgroundsource: NOAA ENC as viewed on Rose Point ECS)
 
RNC for the area near Shakmanof Cove.
The symbol for the rock in the area of the grounding is indicated by a red circle.
(Background Source: NOAA Chart 16594, 14th Edition, January 2015)
visualization with the GeoGarage platform
 
ENC US4AK5PM, for the area near Shakmanof Cove, as viewed by investigators using an equivalent ECS.
The asterisk symbol for the rock in the area of the grounding is indicated by a red circle.
(Background source: NOAA ENC as viewed on Rose Point ECS)
 
The tug's master and mate used the common ECS platform Rose Point, often found on working vessels in Alaska.
The system has a depth contour safety feature that highlights shoals and hazards based on the vessel's draft, but the crew told NTSB they didn't use it.
The reason, they said, was that "the soundings don't really mean anything" in Western Alaska, where much of the survey data dates back many decades.
The software can also highlight isolated dangers, like this submerged rock, but that option was turned off.
The company's policy in its SMS did not require the use of these features, and the firm did not give its crews formal training on the use of the software.
 
US4AK5PM NOAA visualizations
 
"When investigators viewed the area on an equivalent ECS, the asterisk marking the rock was displayed alongside soundings of similar size and color, so it is possible that the captain mistook the asterisk for a depth sounding or other chart information when plotting and reviewing the route," concluded NTSB. "Using other available resources, such as the Coast Pilot, would have helped the captain in identifying the rock when planning and reviewing the route."

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