Streamlit Dashboard

NASA Space Apps Challenge

• High Cost : Transmitting the vast amounts of data collected by spacecraft from celestial bodies back to Earth is very costly, as it requires a significant amount of power to send the data, which is greatly affected by distance.
• Signal Delay : The distance between Earth and celestial bodies causes signal delays. Signal delays can lead to interference, increasing the likelihood of data loss or errors during transmission.

Seismic Detection Across the Solar System

• Slicing with overlaps : To predict seismic events using deep learning, we initially had a small dataset of 77 samples, which increased to about 200 after adding IRIS data. This was still insufficient, so we augmented the data by slicing the seismic data into 6000-step intervals and overlapping points. Shifting the starting point by 100 steps generated multiple samples, ensuring enough data for training.

• Lightweight & efficient : 1D-CNNs are computationally efficient and ideal for reducing energy costs while enabling fast predictions with minimal CPU resources, making them effective for detecting seismic event times.
• Time series pattern recognition : 1D-CNNs learn key patterns in time series data by capturing local correlations through convolution operations, making them effective at detecting changes over time.
• Automatic feature extraction : 1D-CNNs extract important features using filters, offering a more efficient approach than manual feature design, allowing the model to learn various patterns automatically.

• The task is to classify whether a 6000-step section contains an event point (binary classification). A 1D-CNN processes the sequences to learn spatial features, using ReLU activations in the convolution and pooling layers. The output passes through a fully connected layer for binary classification. CrossEntropyLoss and the Adam optimizer are used for training.

• After detecting sections with event points, the next step is to locate the exact event point within the section. A CNN is used to learn patterns and predict the probability of the event at each point, identifying the one with the highest probability. The model shows its strong capability to accurately pinpoint the event's location.

# 1D-CNN Model Definition
def create_1d_cnn_model(input_shape):
    model = Sequential([
        Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=input_shape),
        MaxPooling1D(pool_size=2),
        Conv1D(filters=128, kernel_size=3, activation='relu'),
        MaxPooling1D(pool_size=2),
        Conv1D(filters=256, kernel_size=3, activation='relu'),
        MaxPooling1D(pool_size=2),
        Flatten(),
        Dense(128, activation='relu'),
        Dropout(0.5),
        Dense(1)  # Output Layer: Print Out Event Start Time
    ])
    return mode

Streamlit Dashboard

# Execute virtual environment
python3 -m venv venv
source venv/bin/activate

# install dependencies
pip install -r requirements.txt

# run dashboard
streamlit run predict.py

Lead Scientist : 원하진	Software Engineer : 양은서	Software Engineer : 최다영
Software Engineer : 김태관	Software Engineer : 김태우	Software Engineer : 이원준