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Дефекты рельсов. Найти, используя IT

1 Введение

Для обеспечения безопасности движения на железнодорожном транспорте регулярно проводится неразрушающий контроль рельсов с применением различных подходов и методов. Одним из основных подходов к определению эксплуатационного состояния железнодорожных рельсов является ультразвуковой неразрушающий контроль. Оценка результатов контроля зависит от дефектосокписта. Необходимость снижения нагрузки на человека и повышения эффективности процесса анализа данных ультразвукового контроля, делают задачу создания автоматизированной системы актуальной.

Цель данной работы – оценить возможность создания эффективной системы распознавания дефектов рельсов по дефектограммам ультразвукового контроля методами ML.

2 Анализ предметной области

Железнодорожный путь состоит из рельсовых звеньев, скреплённых между собой посредством болтовых и сварных соединений. При проезде по железнодорожному пути дефектоскопного устройства, оснащённого генерирующими пьезоэлектрическими преобразователями (ПЭП), в рельс с заданной периодичностью излучаются ультразвуковые импульсы. При этом принимающие ПЭП регистрируют отражённые волны. На принципе отражения волн от неоднородностей металла основана выявляемость дефектов ультразвуковым методом, так как трещины, включая и другие неоднородности, отличаются по своему акустическому сопротивлению от остального металла.

2.1 Принцип формирования A-scan

Зарегистрированный сигнал, отражённый от болтового отверстия при перпендикулярном вводе зондирующего импульса к поверхности рельса представлен на рис.1. Изображение такого сигнала называется «Amplitude scan» или сокращённо «А-scan».

Рис.1 – Представление зарегистрированного сигнала ультразвукового контроля на A-scan: a) процесс излучения и регистрации ультразвука, b) регистрируемый сигнал.

Записанная амплитуда такого эхо сигнала на каждой i координате по длине рельса может быть представлена в виде вектора

A_i = [a₁, a₂, a₃, ... , a _j],

где a_j - амплитуда отражённого сигнала на j-ом уровне глубины рельса.

Глубина для каждого значения амплитуды a_j рассчитывается исходя из времени регистрации и частоты излучаемого сигнала.

2.2 Принцип формирования B-scan

Регистрируемые отражённые сигналы А-scan в каждой i точке контроля по длине рельса можно представить в виде двух мерного массива

B = [A₁, A₂, A₃, ... , A_i] размером (i x j).

На рис. 2 схематично представлен фрагмент массива В с записанными эхо сигналами отражения от болтового отверстия при перпендикулярном вводе зондирующего импульса к поверхности рельса.

Рис.2 - Фрагмент массива с сигнализациями болтового отверстия и сигнала дна

Графическое представление двухмерного массива B в виде графика интенсивности называется «Bright-scan» («В-scan») рис.3, при этом значения массива отображаются в трёх измерениях на плоскости путём использования цвета как третьего измерения данных по оси Z.

Рис.3 - Фрагмент В-scan болтового отверстия полученного сканированием при перпендикулярном вводе зондирующего импульса к поверхности рельса (оборудование Авикон-11)

2.3 Формирование дефектограммы

Различные отражающие свойства дефектов, их геометрия и местоположение в рельсе требуют для их обнаружения применения ПЭП с различными углами ввода и регистрации ультразвука. Поэтому при сплошном контроле рельсов современные дефектоскопы используют несколько преобразователей, которые распределены по длине искательной системы дефектоскопа и формируют, так называемую, схему прозвучивания сечений рельса. Одна из применяемых схем контроля представлена на рис. 4, при этом генерирующий и регистрирующий преобразователи каждого угла ввода ультразвука находятся в одном корпусе.

Рис. 4 - Пример схемы излучения ультразвуковых импульсов в рельс с использованием шести преобразователей

Формирование сигналов B-scan для болтового отверстия с использованием преобразователей с центральными углами ввода «+42⁰» (оранжевый цвет), « - 42⁰» (синий цвет) и «0⁰» (зелёный цвет) в трёх характерных точках (1, 2, 3 позициях) по длине рельса схематично представлено на рис. 5a.

Рис.5 – Формирование сигналов при сканировании: a) общий вид b) коррекция со смещением

Информационные каналы дефектоскопа соответствуют физическим датчикам (преобразователям), которые последовательно расположены на поверхности рельса. Совокупность B-scan для всех каналов дефектоскопа каждого рельса объединённая в файл данных называется дефектограммой (сканом). Нередко выделенный для рассмотрения канал или их совокупность также называют дефектограммой.

В большинстве случаев для улучшения восприятия дефектограмма отображается в режиме сведения к единому сечению, при котором корректируются координаты эхо-сигналов для каналов с наклонным вводом ультразвука путём дополнительного учёта отдаления отражателя от точки ввода зондирующего импульса в металл рельса (рис. 5 b). Кроме того, для удобства использования и сокращения графического вида всей дефектограммы производят графическую группировку каналов данных, одна из которых показана на рис. 6.

Рис.6 - Пример участка дефектограммы болтового стыка рельса полученного при сканировании ультразвуковым оборудованием Avicon-11

2.4 Расшифровка дефектограмм (информационные признаки)

Для визуального поиска дефектов на B-scan и A-scan используются когнитивные функции привлекаемых экспертов – дефектоскопистов. При ультразвуковом сканировании рельсов их конструктивные элементы и дефекты имеют акустические отклики, которые отображаются на дефектограмме в виде характерных графических образов. Каждый тип дефекта на дефектограмме является визуально различимым для экспертов в процессе анализа данных. Основная цель анализа дефектограммы заключается в достоверном нахождении и выделении графических образов дефектов на фоне возможных помех и образов конструктивных элементов.

Каждый измерительный канал дефектограммы 0⁰, ±42⁰, ±58⁰, +70⁰ или их совокупность рассчитаны на выявление определённой группы дефектов. Для упрощения задачи поиска дефектов сделаем декомпозицию задачи и рассмотрим возможности алгоритмов DL для поиска отдельных типов дефектных участков по дефектограмме канала «0⁰» дефектоскопа «Avicon-11». При этом типы участков могут быть выделены в четыре класса на основе характерных информационных признаков. Некоторое представление о многообразии набора данных полученного дефектоскопом «Avicon-11» можно получить из Табл.1.

Табл.1 – Примеры экземпляров (B-scan) для выделенных классов (реальные данные)

3 Выбор и реализация алгоритма классификации

Несмотря на то, что в процессе эксплуатации железнодорожного полотна решающее значение имеет наличие или отсутствие дефекта (бинарная классификация), количественно оценим какие дефектные участки имеют большую вероятность быть ложно классифицированными как без дефектные, что является опасным случаем при диагностике рельсов. При этом задача классификации сводится в данной работе к однозначной многоклассовой с четырьмя классами.

3.1 Формирование набора данных

Набор данных набирается из дефектограмм, полученных дефектоскопом «Avicon-11» на нескольких железнодорожных испытательных треках (Railroad Test Track - RTT) и обычных путях при различных условиях. Каждый экземпляр данных представлен в виде прямоугольных данных «глубина × длинна» и имеет форму (224, 1024), что позволяет вместить образы более шести болтовых отверстий по длине рельса при их болтовом стыке.

Формирование набора данных затруднено отсутствием достаточного количества дефектных участков, поэтому для его расширения использовалось смещение по длине рельса и сканирование одного и того же дефекта при различных условиях и настройках испытательного оборудования, что позволяет получить отличающиеся образы дефектов (рис.7).

Рис.7 – Пример увеличения набора данных

результате указанной методики набор данных для классов 0, 1, 2, 3 составляет соответственно 2151, 1043, 1584, 582, а в сумме 5360 экземпляров. Бездефектный класс «0» содержит 10% (214 экз.) экземпляров без болтовых отверстий, остальные 90% (1937 экз.) содержат от одного до шести болтовых отверстий. Набор данных получил название «avicon» и используется в работе только для окончательного тестирования. Это позволяет избежать проблемы дисбаланса классов при обучении и получить более достоверную оценку точности классификатора.

Для целей тренировки и проверки моделей классификации в данной работе используется синтетический, сбалансированный dataset, полученный на основе математического моделирования моделей описывающих процесс отражения и регистрации ультразвуковых волн от конструктивных отражателей рельсов и дефектов. Применение такой обученной модели для классификации реальных данных полученных дефектоскопом при диагностике рельсов демонстрирует рис.8.

Рис.8 – Применение нейронной сети обученной на модельных данных

Примеры синтетических экземпляров выделенных классов представлены в Табл.2. Подробнее о создании синтетических наборов данных можно ознакомиться в работах [1-4].

Табл.2 – Примеры экземпляров (B-scan) для выделенных классов (синтетические данные)

Процесс моделирования позволяет получить значительное количество экземпляров, ограничимся в работе величиной 2048 экземпляров для каждого из синтетических наборов «train», «valid», «test».

Каждый экземпляр данных и метки записывается для каждого набора в соответствующие бинарные файлы images.bin и labels.bin (тип данных «uint8») согласно рис.9.

Рис.9 – Распределение наборов по директориям

3.2 Разведывательный анализ данных

Информация о количестве данных, балансе классов для синтетических наборов и набора «avicon» представлена на рис.10.

Рис.10 – Сводная информация о наборах данных

Анализ графического представления фреймов реальных данных позволяет выявить, по крайней мере, одно важное свойство дефектов 3 класса: образы дефектов наиболее трудно отличимы от образов болтовых отверстий, особенно если находятся на одном и том же уровне по глубине рельса, что в значительной степени затрудняют задачу классификации.

Каждый экземпляр данных имеет размер 224 х 1024 и является достаточно большим для применения алгоритмов ML, вызывая трудности организации процесса обучения. Каждый такой экземпляр можно считать точками данных в 224*1024 = 229376-мерном пространстве, которое является высоко разряженным так как содержит достаточно большое количество нулевых значений. Построенный график интегральной объяснимой дисперсии набора «train» как функции от количества компонент метода PCA (рис. 11) показывает, что при использовании 1000 компонент (в 228376 / 1000 = 330 раз меньше исходного размера) объясняется уже 98.5% дисперсии, что свидетельствует о высоком уровне избыточности в исходных данных. Такой уменьшенный набор данных может использоваться в алгоритмах ML, однако его получение на всём наборе данных одновременно вызывает затруднения, поэтому далее в работе рассматривается алгоритм, основанный на Deep Learning.

Рис.11 – График интегральной объяснимой дисперсия данных как функция количества компонент метода PCA для наборе данных «train»

3.3 Архитектура нейронной сети

В работе рассматривается модель DL в виде линейного стека слоёв (рис.12а – окончательный вариант сети). Функция активации: relu (rectified linear unit), для выходного полносвязного слоя - нормализованная экспоненциальная функция softmax, при которой сумма значений всех выходных нейронов равна единице. Функция потерь: мера ошибки в виде расстояния между распределениями вероятностей фактических данных и их прогнозом (перекрёстная энтропия). Оптимизатор: алгоритм стохастического градиентного спуска в модификации RMSProp. Метрики в процессе обучения: точность, как величина равная отношению количества правильно классифицированных объектов к общему числу объектов.

3.4 Обучение сети

Обучение окончательного варианта сети проводилось в течении 50 эпох. Графики изменения качественных показателей «loss» и «accuracy» характеризующих процесс обучения (рис. 12b) сходятся на этапе обучения и валидации и имеют низкое и высокое значения соответственно, что может свидетельствовать об отсутствии эффекта переобучения модели. Этот факт подтверждает и относительное равенство полученных точностей прогноза модели на наборе «train» - 99.61% и наборе «test» - 99.02% (рис.12b,c). Занимаемая память сети в формате H5 составляет 30 Кбайт. Полный код можно найти в репозитории Github по ссылке [5].

Рис.12 - NN и результаты её обучения: а) архитектура сети; b) Изменение «Loss» and «Accuracy» при обучении; с) Classification Report; d) Confusion matrix

Полученная confusion matrix и сводный отчёт о качестве модели представлен на рис. 12d,c. Обученная модель обладает высокими показателями precesion и recall выше 96% для всех классификаторов классов, что также означает достаточность информационных признаков в данных для классификации.

Важным для понимания работы классификатора и его изменения является рассмотрение ложно классифицированных образцов. Согласно confusion matrix классификатор 3 класса ошибочно распознал 4 образца класса 0, которые имеют как минимум одну сигнализацию болтового отверстия схожую с образом дефекта 3 группы (пример на рис.13а), что возможно и стало причиной ошибки.

Классификатор класса 0 ошибочно распознал два образца класса 1. Оба неправильно распознанных дефекта имеют характерный внешний вид и расположены крайне близко к верней границы кадра данных. Одни из таких кадров – рис.13b.

Классификатор класса 0 ошибочно распознал один образец класса 2, который расположен достаточно близко к глубине нахождения болтовых отверстий (рис.13с).

Классификатор класса 0 ошибочно распознал 13 образцов класса 3, который расположен достаточно близко к глубине нахождения болтовых отверстий (рис.13d).

По результатам тестов работы сети отмечается сложность различия дефекта 3 класса с болтовыми отверстиями.

Рис.13 – Характерные фреймы ошибочно классифицированных данных

4 Оценка эффективности сети на реальных данных («avicon»)

Для оценки качества работы обученной нейронной сети по распознанию экземпляров реальных данных полученных дефектоскопом «Avicon-11» было проведено моделирование на наборе данных «avicon». Точность всей сети составила 90%, что на 9% ниже точности прогноза для синтетических данных. Полученная матрица различий и сводный отчёт о качестве модели представлены на рис. 14. Затраты времени на классификацию размеченных данных позволяют оценить время необходимое для классификации 100 км железнодорожного пути - 11 с.

Рис.14 – Сводный отчёт о качестве модели на основе классификации набора данных «avicon»

Проведём анализ наиболее важных ошибок классификации данных.

Согласно confusion matrix (рис.14) четыре поперечных трещины с максимально слабым
откликом регистрируемые каналом 0⁰ и относящиеся к классу 1 были классифицированы как класс 0 (без дефекта). Для улучшения распознавания подобных дефектов требуется добавление дополнительных информационных признаков, которые можно получить из наклонных каналов дефектоскопа, являющиеся основными каналами для обнаружения подобных видов дефектов (рис. 15).

Рис.15 – Характерные фреймы ошибочно классифицированных данных (True =1, Predict = 0)

Ошибочная классификация 49 дефектов 2 класса как бездефектных, связана со слабыми откликами сигналов, регистрируемыми измерительным каналом 0⁰. Одним из способов улучшения классификации подобных образцов класса 2 является рассмотрение дополнительных информационных признаков наклонных каналов (Ch ±42⁰), так они являются основными каналами для обнаружения не правильно классифицированных дефектов (рис. 16).

Рис.16 – Характерный ошибочно классифицированный фрейм данных 2 класса (True =2, Predict = 0)

Ошибочная классификация 112 образцов 3 класса в класс 0 связана с фреймами данных, где образ дефекта:

- находится на уровне болтовых отверстий,

- располагается ближе к краю кадра данных,

- имеет схожесть с образом болтовых отверстий.

Ошибочная классификация 152 образцов 0 класса в класс 3 связана с аналогичной причиной – схожесть образов болтовых отверстий с образами дефектов 3 класса.

Одним из способов улучшения классификации образцов класса 0 и 3 является рассмотрение дополнительных информационных признаков наклонных каналов (Ch ±42⁰),
так как в этом случает болтовые отверстия хорошо отличимы от дефекта 3 класса и наоборот (рис. 17).

Рис.17 – Характерный ошибочно классифицированный фрейм данных 3 класса (True =3, Predict = 0)

Графические образы дефектов 1 и 2 класса схожи и отнесение дефекта к классу 1 или 2 зависит от глубины, с которой начинается регистрация образа дефекта на дефектограмме. Дефекты класса 1 находятся в головке рельса, дефекты класса 2 могут регистрироваться, начиная с зоны перехода рельса из его головки в зону шейки. Ошибочная классификация 165 дефектов 1 класса как дефекты класса 2 скорее всего связана со слабыми откликами дефекта регистрируемыми в зоне головки рельса (рис. 18).

Рис.18 – Характерные ошибочно классифицированные фреймы данных 1 класса (True =1, Predict = 2)

4.1 Бинарный классификатор

Одной из важных задач полученного классификатора при его практическом использовании будет точное определение без дефектного класса (класс 0), что позволит исключить ложное отнесения дефектных образцов к без дефектным. Снизить количество ложных срабатываний для классификатора 0 класса возможно за счёт изменения порога отсечения вероятности. Для оценки применимого порогового уровня отсечения проведена бинаризация многоклассовой задачи с выделением бездефектного состояния и всех дефектных состояний, что соответствует стратегии «один против остальных» (One vs Rest). По умолчанию для двоичной классификации пороговое значение принимается равным 0.5 (50%). При таком походе бинарный классификатор имеет показатель точности равный 92.28% (рис. 19).

Рис. 19 – Качественные показатели бинарного классификатора при пороге отсечения 0,5 (набор «avicon»)

Изменения прецизионности и полноты бинарного классификатора в зависимости от изменяющегося порога отсечения представлено графиком «precision-recall curve» (рис. 20a). При пороге отсечения 0.5 величина ложноположительных срабатываний составляет 161 образец (рис. 20b). Увеличение порога отсечения до уровня 0.8 и 0.9 позволяет снизить количество ложноположительных срабатываний до величины 70 и 58 соответственно, за счёт увеличения ложноотрицательных срабатываний до 344 и 440 (рис. 20b).

Можно сказать, что при автоматическом анализе увеличение порога отсечения позволяет, с одной стороны, уменьшить ложное отнесение дефектов к бездефектному состоянию и тем снизить риск пропуска дефектов, с другой стороны, увеличивает трудоёмкость человека при
ручном анализе фреймов с заведомо отсутствующими дефектами.

Рис.20 – Влияние порога отсечения на характеристики бинарного классификатора: а) precision-recall curve, b) confusion matrix при различных порогах отсечения.

5 Выводы

1. На основе анализа предметной области ультразвукового обследования рельсов выявлены информационные признаки дефектов, позволяющие выделить четыре класса участков рельсов для проведения их классификации методами машинного обучения.

2. Собран и аннотирован набор данных ультразвукового обследования рельсов, который включает 5360 экземпляров.

3. Созданы синтетический обучающий, тестовый и валидационный наборы данных на основе стохастического математического моделирования моделей, описывающих процесс отражения и регистрации ультразвуковых волн от конструктивных отражателей рельсов и дефектов.

4. Для решения задачи однозначной многоклассовой классификации обучена структура нейронной сети на основе свёрточной модели с общей точностью 99%.

5. Подтверждена эффективность применения обученной на модельных данных нейронной сети к распознаванию образов реальных дефектов рельсов.

6. Дана оценка достижимой точности классификации в 90%, используя только участки дефектограмм нулевого канала ультразвукового дефектоскопа.

7. Проведён анализ причин ошибок нейронной сети и показана необходимость применения дополнительных информационных признаков из дефектограмм наклонных каналов дефектоскопа.

References

[1] Kaliuzhnyi A. Application of Model Data for Training the Classifier of Defects in Rail Bolt Holes in Ultrasonic Diagnostics. Artificial Intelligence Evolution [Internet]. 2023 Apr. 14 [cited 2023 Jul. 28];4(1):55-69. Available from: https://ojs.wiserpub.com/index.php/AIE/article/view/2339. DOI: https://doi.org/10.37256/aie.4120232339

[2] Kaliuzhnyi A. Application of Machine Learning Methods To Search for Rail Defects (Part 2). Available from: https://dzone.com/articles/application-of-machine-learning-methods-to-search

[3] Kaliuzhnyi A. Using Machine Learning To Detect Railway Defects. Available from: https://dzone.com/articles/use-of-mathematical-modeling-to-increase-the-data

[4] NVIDIA Blog. «What Is Synthetic Data? Synthetic data generated from computer simulations or algorithms provides an inexpensive alternative to real-world data that’s increasingly used to create accurate AI models». Available from: https://blogs.nvidia.com/blog/2021/06/08/what-is-synthetic-data/

[5] https://github.com/kaazdes/ClassBscanZeroCh_Avicon_public

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29 янв. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
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HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.
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23.09.24 18:56 matthewshimself

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02.10.24 22:27 Emily Hunter

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31.10.24 00:13 ytre89

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02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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21.11.24 08:02 Emily Hunter

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22.11.24 23:43 teresaborja

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I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

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26.11.24 23:12 rickrobinson8

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27.11.24 00:39 [email protected]

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27.11.24 09:10 Michal Novotny

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01.12.24 17:21 KollanderMurdasanu

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02.12.24 23:02 ytre89

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04.12.24 22:24 andreygagloev

When I first heard about Bitcoin back in 2018, I was skeptical. The idea of a decentralized, digital currency seemed too good to be true. But I was intrigued as I learned more about the technology behind it and its potential. I started small, investing just a few hundred dollars, dipping my toes into the cryptocurrency waters. At first, it was exhilarating to watch the value of my investment grow exponentially. I felt like I was part of the future, an early adopter of this revolutionary new asset. But that euphoria was short-lived. One day, I logged into my digital wallet only to find it empty - my Bitcoin had vanished without a trace. It turned out that the online exchange I had trusted had been hacked, and my funds were stolen. I was devastated, both financially and emotionally. All the potential I had seen in Bitcoin was tainted by the harsh reality that with decentralization came a lack of regulation and oversight. My hard-earned money was gone, lost to the ether of the digital world. This experience taught me a painful lesson about the price of trust in the uncharted territory of cryptocurrency. While the technology holds incredible promise, the risks can be catastrophic if you don't approach it with extreme caution. My Bitcoin investment gamble had failed, and I was left to pick up the pieces, wiser but poorer for having placed my faith in the wrong hands. My sincere appreciation goes to MUYERN TRUST HACKER. You are my hero in recovering my lost funds. Send a direct m a i l ( muyerntrusted ( @ ) mail-me ( . )c o m ) or message on whats app : + 1 ( 4-4-0 ) ( 3 -3 -5 ) ( 0-2-0-5 )
12.12.24 00:35 amandagregory

HOW TO HIRE A HACKER TO RECOVER STOLEN BITCOIN WITH FASTFUND RECOVERY
12.12.24 00:35 amandagregory

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19.12.24 17:07 rebeccabenjamin

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