Private user posted an update — Oct 11, 2025 10:59 AM EDT

تایج آزمون جامع سیستم پیزوالکتریک هوشمند

دقت: 0.9820 (98.2٪) دقت: 0.9655 ادآوری: 0.9750 -امتیاز: 0.9702 سرعت (تأخیر): 10 میلی ثانیه آموزش: بله (بدون نیاز به آموزش پیچیده) پایداری (دقت با 30٪ نویز): 0.9410 (94.1٪) مصرف انرژی: 40.0 pJ (≈ 40 عکس)

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Private user posted an update — Sep 20, 2025 05:01 PM EDT

Adding a piezoelectric sensor with memory to smart circuits and controlling it by AI is a great idea. It can significantly expand the capabilities of the system. Advanced data processing capabilities One of the most important benefits of integrating AI with sensors is the increased data processing capabilities, which can: 1) Advanced data processing capabilities 2) Predictive maintenance and diagnostics 3) Intelligent adaptive systems 4) Advanced pattern recognition and anomaly detection 5) AI-based automation Applications of the invention's integrated and flexible sensors across industries, now this invention is made possible by placing and integrating piezoelectrics as the basis of smart circuits that deform under magnetic field conditions, and environmental conditions including light, temperature and pressure, and are controlled and programmed by AI. It is considered one of the masterpieces of the electronics world and can be used in all fields of industry. Including 1) Environmental monitoring 2) Healthcare 3) Automotive industry 4) Agriculture 5) Industrial automation 6) Electric skin 7) Robotics 8) Mobile phones It is used and is a significant leap in the evolution of smart systems. By embedding artificial intelligence in itself, we can enhance its capabilities and make data processing faster, more accurate and more reliable. Smart sensors not only meet today's needs, but also anticipate future challenges. As a result, the convergence of technology, artificial intelligence and sensors is not just a trend. But a fundamental change that redefines the way we interact with the world around us. This invention drives innovation, efficiency and sustainability across industries.

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Private user posted an update — Sep 20, 2025 12:11 PM EDT

Part One: Introduction: Scientific explanation of the fundamental biological solutions of the invention: Since the emergence of artificial intelligence, which has been modeled and inspired by the human brain. In recent years, many researchers have conducted research and studies in this regard to bring information processing and brain behavior closer to artificial intelligence, but so far no interesting approach has been found. The similarities made so far have mostly been in the form of improvements. Due to the self-learning nature of neural networks that improve through experience and data repetition. Neural networks are known as powerful tools in artificial intelligence. Artificial neural networks or ANNs are a branch of machine learning models that have been created with the help of the principles of organizing neurons of living organisms, neural networks and processing nodes that follow the process of examining information.

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Private user posted an update — Sep 18, 2025 04:16 PM EDT

┌─────────────────────────────┐لایه پیش پردازش شبکه عصبی دو نیمکره ای │ RAW INPUT X │ MODULATION& SIGNAL └──────────────┬───────────────┘ │تقویت NOISE REDUCTION ┌─────────────▼─────────────┐ │ MULTI-HEAD SELF-ATTENTION │ ← مکانیزم توجه پیشرفته فیلترگذاری ،دسته بندی └─────────────┬─────────────┘ │ ┌─────────────▼─────────────┐ │ SIGNAL MODULATION & │ ← تقویت سیگنال + حذف نویز │ NOISE REDUCTION │ └─────────────┬─────────────┘ │ PROJECTION NEURONS │ ← تبدیل به دو بردار مجزا FORWARD() ┌─────────────▼─────────────┐ │ ← PLASTICITY TRAINING └──────┬─────────┬──────────┘ │ │ ┌────────▼─┐ ┌───▼────────┐ │ X_R │ │ X_L │ └──┬───────┘ └───────┬────┘ │ │ ┌───────────▼───┐ ┌───────▼────────┐ │ RIGHT HEMISPHERE│ │ LEFT HEMISPHERE│ │ NONLINEAR PROC. │ │ LINEAR PROC. │ └──┬──────────────┘ └──────────┬─────┘ │ │ ┌───────▼─────────┐ ┌────────▼───────┐ │ R → L LINK │ │ L → R LINK │ └───────┬─────────┘ └────────┬───────┘ │ │ ┌──▼────────────────────────────────▼──┐ │ FUSION LAYER │ └──────────────────┬───────────────────┘ │ ┌─────▼─────┐ │ OUTPUT │ └───────────┘ X_R ──[ W2 · X_R + b2 → f_non_linear ]───┐ │ Cross-Connection ▼ [Fusion Layer] → Output ▲ Cross-Connection │ X_L ──[ W3 · X_L + b3 ]──────────────────┘ Raw Input │ [ Attention + Modulation + Noise Reduction ] │ [ Projection Layer ] → X_R (Right), X_L (Left) │ │ │ │ ▼ ▼ Right Hemisphere Left Hemisphere (Nonlinear) (Linear) │ ↔ Cross Connections └──────► Fusion Layer ◄───────┘ │ Output

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Private user posted an update — Sep 18, 2025 07:52 AM EDT

Medical images, medical outcome prediction, drug prescription, real-time decision-making in robotics and industries, intelligent networks, complex pattern processing with high interpretability, robotics

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Private user posted an update — Sep 18, 2025 03:10 AM EDT

I helped with the initial programming work, and if this project becomes a global partnership and the entire project is unveiled, it will be the greatest invention of the century.

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Private user posted an update — Sep 07, 2025 06:10 PM EDT

Electronic skin: Flexible materials controlled by artificial intelligence. And can act as skin sensors 2) Intelligent communication networks: Systems that adjust their performance based on environmental changes and provide optimal communications 3) High-performance image and video processing with low energy consumption 4) Environmental monitoring 5) Security systems: Circuits that change their performance according to changes in temperature or light of the environment 6) Health and treatment and medical care and drug prescription 7) Medical image processing and diagnosis of cancerous tumors 8) Industrial automation 9) Troubleshooting industrial and technical systems 10) Self-driving and smart vehicles and increasing accuracy in diagnosis 11) Economic and financial matters and financial market forecasting 12) Detection of damage and cracks in structures 13) Autonomous flight 14) Agriculture, monitoring soil conditions, water quality and environmental factors

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Private user posted an update — Sep 07, 2025 09:50 AM EDT

Bihemispheric neural network # First layer#

Attention mechanism, signal processing modulation=filter+denoising+classification=2output vector=nonlinear patterns/linear pattern=transition to hidden layer/projection neurons

EnhancedBiHemisphericLayer(nn.Module): def init(self, in_channels, out_channels): super().init()

Preprocessing layer - denoising

self.denoise = nn.Sequential( nn.Conv1d(in_channels, in_channels//2, kernel_size=3, padding=1), nn.ReLU(), nn.Dropout(0.1) )

Two hemispheres

self.left_hemisphere = nn.Sequential( nn.Conv1d(in_channels//2, out_channels, kernel_size=5, padding=2), nn.LayerNorm([out_channels, 100]), nn.ReLU() )

self.right_hemisphere = nn.Sequential( nn.Conv1d(in_channels//2, out_channels, kernel_size=3, padding=1), nn.LayerNorm([out_channels, 100]), nn.ReLU() )

Merge hemispheres

self.fusion = nn.Sequential( nn.Conv1d(out_channels*2, out_channels, kernel_size=1), nn.ReLU() )

def forward(self, x):

Preprocess and denoise

cleaned = self.denoise(x)

Parallel processing in two hemispheres

left_out = self.left_hemisphere(cleaned) right_out = self.right_hemisphere(cleaned)

Merge and return

combined = torch.cat([left_out, right_out], dim=1) return self.fusion(combined

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Private user posted an update — Sep 07, 2025 04:07 AM EDT

import torch.nn as nn

class OptimizedMLP(nn.Module): def init(self, input_size, hidden_sizes, output_size, dropout=0.1): super().init() layers = [] in_features = input_size

    for hidden_size in hidden_sizes:
        layers.extend([
            nn.Linear(in_features, hidden_size),
            nn.LayerNorm(hidden_size),
            nn.ReLU(inplace=True),
            nn.Dropout(dropout)
        ])
        in_features = hidden_size

    # اضافه کردن LayerNorm و Dropout برای لایه خروجی
    layers.extend([
        nn.Linear(in_features, output_size),
        nn.LayerNorm(output_size),
        nn.Dropout(dropout)
    ])

    self.net = nn.Sequential(*layers)

def forward(self, x):
    return self.net(x)

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Private user posted an update — Sep 07, 2025 04:05 AM EDT

import torch.nn as nn

class OptimizedMLP(nn.Module): def init(self, input_size, hidden_sizes, output_size, dropout=0.1): super().init() layers = [] in_features = input_size

    for hidden_size in hidden_sizes:
        layers.extend([
            nn.Linear(in_features, hidden_size),
            nn.LayerNorm(hidden_size),
            nn.ReLU(inplace=True),
            nn.Dropout(dropout)
        ])
        in_features = hidden_size

    # اضافه کردن LayerNorm و Dropout برای لایه خروجی
    layers.extend([
        nn.Linear(in_features, output_size),
        nn.LayerNorm(output_size),
        nn.Dropout(dropout)
    ])

    self.net = nn.Sequential(*layers)

def forward(self, x):
    return self.net(x)

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Private user posted an update — Sep 06, 2025 05:15 AM EDT

The codes have been debugged and we are working on fixing them.

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Private user posted an update — Sep 06, 2025 04:06 AM EDT

Friends, I am the person in charge of this Nader Maleki project. It has been reported that there are errors in the program codes. I have made the necessary coordination to ensure that the error is resolved immediately. The reason for any error or manipulation in the repository will be determined and announced soon. Thank you.

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Private user posted an update — Sep 06, 2025 12:49 AM EDT

test_piezoelectric_report.py

import unittest import os import tempfile import shutil import json import pandas as pd import numpy as np from unittest.mock import patch, MagicMock import matplotlib.pyplot as plt

Import the functions from the main code (after refactoring)

from main_code import ( create_title_page, process_seed_data, simulate_piezoelectric_sensor, create_innovation_page, generate_report )

class TestPiezoelectricReport(unittest.TestCase):

def setUp(self):
    """Set up test environment"""
    self.test_dir = tempfile.mkdtemp()
    self.results_dir = os.path.join(self.test_dir, 'results')
    os.makedirs(self.results_dir, exist_ok=True)

    # Create test data files
    self.create_test_files()

def tearDown(self):
    """Clean up test environment"""
    shutil.rmtree(self.test_dir)
    plt.close('all')

def create_test_files(self):
    """Create test JSON, CSV files for testing"""
    # Create metrics JSON files
    for i in range(2):
        metrics_data = {
            'test_accuracy': 0.85 + i*0.05,
            'f1_score': 0.82 + i*0.04
        }
        with open(os.path.join(self.results_dir, f'metrics_seed{i}.json'), 'w') as f:
            json.dump(metrics_data, f)

    # Create history CSV files
    for i in range(2):
        history_data = {
            'epoch': range(10),
            'accuracy': [0.1*j for j in range(10)],
            'val_accuracy': [0.08*j for j in range(10)]
        }
        df = pd.DataFrame(history_data)
        df.to_csv(os.path.join(self.results_dir, f'history_seed{i}.csv'), index=False)

    # Create confusion matrix CSV files
    for i in range(2):
        confmat_data = np.array([[5, 2], [1, 7]])
        df = pd.DataFrame(confmat_data)
        df.to_csv(os.path.join(self.results_dir, f'confmat_seed{i}.csv'), index=False)

def test_create_title_page(self):
    """Test title page creation"""
    fig = create_title_page()
    self.assertIsInstance(fig, plt.Figure)
    self.assertEqual(len(fig.axes), 1)

def test_process_seed_data(self):
    """Test processing of seed data"""
    metrics_file = os.path.join(self.results_dir, 'metrics_seed0.json')
    hist_file = os.path.join(self.results_dir, 'history_seed0.csv')
    conf_file = os.path.join(self.results_dir, 'confmat_seed0.csv')

    # Mock PdfPages to capture saved figures
    with patch('matplotlib.backends.backend_pdf.PdfPages') as mock_pdf:
        mock_pdf_instance = MagicMock()
        mock_pdf.return_value.__enter__.return_value = mock_pdf_instance

        process_seed_data(metrics_file, hist_file, conf_file, mock_pdf_instance)

        # Check that savefig was called 3 times (curves, confmat, text)
        self.assertEqual(mock_pdf_instance.savefig.call_count, 3)

def test_simulate_piezoelectric_sensor(self):
    """Test piezoelectric sensor simulation"""
    # Mock PdfPages
    with patch('matplotlib.backends.backend_pdf.PdfPages') as mock_pdf:
        mock_pdf_instance = MagicMock()
        mock_pdf.return_value.__enter__.return_value = mock_pdf_instance

        fig = simulate_piezoelectric_sensor()

        self.assertIsInstance(fig, plt.Figure)
        self.assertEqual(len(fig.axes), 3)  # Should have 3 subplots

def test_simulate_piezoelectric_calculations(self):
    """Test the calculations in piezoelectric simulation"""
    # Test parameters
    params = {
        'd33': 2.5e-10,
        'spring_k': 1000,
        'damping_c': 10,
        'capacitance_C': 1e-6,
        'radius': 0.01
    }

    # Test data
    time = np.linspace(0, 1, 100)
    x = 0.01 * np.sin(2 * np.pi * 5 * time)
    dx_dt = np.gradient(x, time)

    # Calculations
    cross_section = np.pi * params['radius'] ** 2
    F_mech = params['spring_k'] * x + params['damping_c'] * dx_dt
    sigma = F_mech / cross_section
    Q = params['d33'] * F_mech
    V = Q / params['capacitance_C']

    # Assertions
    self.assertEqual(len(V), 100)
    self.assertEqual(len(F_mech), 100)
    self.assertEqual(len(sigma), 100)
    self.assertTrue(np.all(np.isfinite(V)))
    self.assertTrue(np.all(np.isfinite(F_mech)))
    self.assertTrue(np.all(np.isfinite(sigma)))

def test_create_innovation_page(self):
    """Test innovation page creation"""
    fig = create_innovation_page()
    self.assertIsInstance(fig, plt.Figure)
    self.assertEqual(len(fig.axes), 1)

@patch('matplotlib.backends.backend_pdf.PdfPages')
@patch('glob.glob')
def test_generate_report_integration(self, mock_glob, mock_pdf):
    """Test the complete report generation process"""
    # Mock glob to return our test files
    mock_glob.side_effect = [
        sorted([os.path.join(self.results_dir, f'metrics_seed{i}.json') for i in range(2)]),
        sorted([os.path.join(self.results_dir, f'history_seed{i}.csv') for i in range(2)]),
        sorted([os.path.join(self.results_dir, f'confmat_seed{i}.csv') for i in range(2)])
    ]

    # Mock PdfPages
    mock_pdf_instance = MagicMock()
    mock_pdf.return_value.__enter__.return_value = mock_pdf_instance

    # Generate report
    pdf_path = generate_report(self.results_dir)

    # Verify PDF was created
    self.assertTrue(pdf_path.endswith('summary_dual_hemisphere_2031.pdf'))

    # Verify savefig was called multiple times (title + 2 seeds * 3 + sensor + innovations)
    expected_calls = 1 + (2 * 3) + 1 + 1  # title + seeds + sensor + innovations
    self.assertEqual(mock_pdf_instance.savefig.call_count, expected_calls)

def test_file_reading(self):
    """Test that files can be read correctly"""
    # Test JSON reading
    with open(os.path.join(self.results_dir, 'metrics_seed0.json'), 'r') as f:
        metrics = json.load(f)
    self.assertIn('test_accuracy', metrics)
    self.assertIn('f1_score', metrics)

    # Test CSV reading
    history = pd.read_csv(os.path.join(self.results_dir, 'history_seed0.csv'))
    self.assertIn('epoch', history.columns)
    self.assertIn('accuracy', history.columns)
    self.assertIn('val_accuracy', history.columns)

    confmat = pd.read_csv(os.path.join(self.results_dir, 'confmat_seed0.csv'), index_col=0)
    self.assertEqual(confmat.shape, (2, 2))

if name == 'main': unittest.main()

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Private user posted an update — Sep 06, 2025 12:43 AM EDT

test_simple.py

import unittest import os import tempfile import shutil import json import pandas as pd import numpy as np

class TestBasicFunctionality(unittest.TestCase):

def setUp(self):
    self.test_dir = tempfile.mkdtemp()

def tearDown(self):
    shutil.rmtree(self.test_dir)

def test_file_creation(self):
    """Test basic file creation and reading"""
    # Create a test JSON file
    test_data = {"test_accuracy": 0.85, "f1_score": 0.82}
    test_file = os.path.join(self.test_dir, "test.json")

    with open(test_file, 'w') as f:
        json.dump(test_data, f)

    # Read it back
    with open(test_file, 'r') as f:
        loaded_data = json.load(f)

    self.assertEqual(loaded_data["test_accuracy"], 0.85)
    self.assertEqual(loaded_data["f1_score"], 0.82)

def test_dataframe_creation(self):
    """Test DataFrame functionality"""
    data = {
        'epoch': [1, 2, 3],
        'accuracy': [0.1, 0.2, 0.3],
        'val_accuracy': [0.08, 0.18, 0.28]
    }
    df = pd.DataFrame(data)

    self.assertEqual(len(df), 3)
    self.assertIn('accuracy', df.columns)

if name == 'main': unittest.main()

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Private user posted an update — Sep 05, 2025 10:40 PM EDT

Artificial Intelligence Network with 3D and Dual-Hemispheric Processing Inventor and Innovator: Nader Malik International Patent Registration Number: 140450140003002031June 9, 1404 PCT/IR2025/050026

Technical problems in the field of artificial intelligence neural networks in the inefficient processing of complex patterns, high cost of calculations, optimal energy consumption, memory consumption, more training time and interpretability are important problems in this field. Building neural networks from the functioning of the human brain is exactly based on the foundation of building artificial intelligence and this scientific implementation is close to simulating biological artificial intelligence. In the brain, data is sent as a signal in the thalamus to the two hemispheres of the brain with respect to amplification, filtering, classification and processing. And the two hemispheres with separate and coordinated tasks and division of complex patterns. For example: When reading a text, the left hemisphere processes words and sentences, and the right hemisphere helps to understand the overall meaning of the text and its relationship to the individual's previous knowledge. This is an undeniable scientific fact. In a 3D network with 3D processing, the next data signals in the first layer are pre-processed with the attention and modulation mechanism, amplified, filtered and de-noised, categorized and given in two weight vectors with separate outputs by the neurons projecting to the hidden layer which is given as a two-hemisphere architecture. The right output to the right hemisphere is nonlinear patterns in hyperbolic space and the left output to the left hemisphere is linear models which are separately and in coordination with the simulation of the corpus callosum in the neural network through the mechanisms of placement like an intermediate layer. From plasticity training, synaptic, reinforcement, deep and modulation signal processing methods are used to train the network. Using piezoelectric memory sensors and circuit bases that are deformed under the influence of environmental conditions (temperature, light, heat) and magnetic fields and controlled, monitored and programmed by the artificial intelligence coordinating module. Have high and efficient reception. Considering the submission of this abstract to the openAIba and Amazon competitions, this project is inspired and implemented by the eagle's optic nerve in observation and recognition.

Patent No. 140450140003002031

Inventor Nader Maleki

import os import glob import json import numpy as np import pandas as pd import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages from sklearn.metrics import classification_report from datetime import datetime

plt.style.use('seaborn-v0_8')

---------- Part 1: Multi-run network processing ----------

results_dir = "results" os.makedirs(results_dir, exist_ok=True)

Reading files by listing once

all_files = glob.glob(os.path.join(results_dir, '*')) seed_metrics = sorted([f for f in all_files if 'metrics_seed' in f and f.endswith('.json')]) seed_histories = sorted([f for f in all_files if 'history_seed' in f and f.endswith('.csv')]) seed_confmats = sorted([f for f in all_files if 'confmat_seed' in f and f.endswith('.csv')])

Create PDF

pdf_path = os.path.join(results_dir, 'summary_dual_hemisphere_2031.pdf')

with PdfPages(pdf_path) as pdf:

Title page

fig, ax = plt.subplots(figsize=(11, 8.5)) ax.axis('off') ax.text(0.5, 0.6, "Dual Hemisphere Network Report – Code 2031", fontsize=20, ha='center', fontweight='bold') ax.text(0.5, 0.4, f"Generation date: {datetime.now().strftime('%Y-%m-%d %H:%M')}", fontsize=14, ha='center') pdf.savefig(fig) plt.close()

Process each run

for metrics_file, hist_file, conf_file in zip(seed_metrics, seed_histories, seed_confmats): seed_id = os.path.splitext(os.path.basename(metrics_file))[0].split("_")[-1]

Read data

with open(metrics_file, 'r', encoding='utf-8') as f: metrics = json.load(f)

history = pd.read_csv(hist_file) confmat = pd.read_csv(conf_file, index_col=0).values

Generate classification report

y_true = np.repeat(range(confmat.shape[0]), confmat.sum(axis=1)) y_pred = np.repeat(range(confmat.shape[1]), confmat.sum(axis=0))

report_text = ( f"Seed results {seed_id}:\n" f"Accuracy: {metrics.get('test_accuracy', 'N/A'):.4f}\n" f"F1 score: {metrics.get('f1_score', 'N/A'):.4f}\n" f"Classification report:\n{classification_report(y_true, y_pred)}" )

Training curves

fig, ax = plt.subplots(figsize=(8, 5)) ax.plot(history['epoch'], history['accuracy'], label='Training accuracy') ax.plot(history['epoch'], history['val_accuracy'], label='Accuracy Validation') ax.set_title(f'Learning Curves – Seed {seed_id}') ax.set_xlabel('Period') ax.set_ylabel('Accuracy') ax.legend() pdf.savefig(fig) plt.close()

Confusion Matrix

fig, ax = plt.subplots(figsize=(6, 5)) im = ax.imshow(confmat, cmap='viridis', aspect='auto') plt.colorbar(im, ax=ax) ax.set_title(f'Confusion Matrix – Seed {seed_id}') pdf.savefig(fig) plt.close()

Text Page

fig, ax = plt.subplots(figsize=(8.5, 11)) ax.axis('off') ax.text(0, 1, report_text, va='top', family='monospace', fontsize=8) pdf.savefig(fig) plt.close()

---------- Part 2: Piezoelectric Sensor Simulation ----------

Parameters (constant values)

PARAMS = { 'd33': 2.5e-10, # C/N 'thickness_m': 0.001, # m 'spring_k': 1000, # N/m 'damping_c': 10, # Ns/m 'capacitance_C': 1e-6, # Farad 'radius': 0.01 # m }

Time and Displacement

time = np.linspace(0, 1, 500) x = 0.01 * np.sin(2 * np.pi * 5 * time) dx_dt = np.gradient(x, time)

Calculations

cross_section = np.pi * PARAMS['radius'] ** 2 F_mech = PARAMS['spring_k'] * x + PARAMS['damping_c'] * dx_dt sigma = F_mech / cross_section Q = PARAMS['d33'] * F_mech V = Q / PARAMS['capacitance_C']

Plot graphs

fig, axs = plt.subplots(3, 1, figsize=(10, 12), sharex=True)

axs[0].plot(time, V * 1e3) axs[0].set_ylabel('Voltage (mV)') axs[0].set_title('Piezoelectric voltage vs time') axs[0].grid(True, alpha=0.3)

axs[1].plot(time, F_mech) axs[1].set_ylabel('Force (N)') axs[1].set_title('Mechanical force vs time') axs[1].grid(True, alpha=0.3)

axs[2].plot(time, sigma / 1e6) axs[2].set_ylabel('Pressure (MPa)') axs[2].set_xlabel('Time (s)') axs[2].set_title('Pressure vs time') axs[2].grid(True, alpha=0.3)

plt.tight_layout() pdf.savefig(fig) plt.close()

---------- Section 3: Innovations ----------

innovation_text = """ Model innovations:

1. Combining a bi-hemispheric neural network with an array of intelligent piezoelectric sensors for simultaneous processing of mechanical and electrical data.
2. Physical model including spring, damping and capacitance to simulate more accurately the behavior Sensor.
3. Automatic multi-run reporting mechanism combining machine learning results and sensor circuits into a comprehensive PDF file. """

fig, ax = plt.subplots(figsize=(8.5, 11)) ax.axis('off') ax.text(0.05, 0.95, innovation_text, va='top', family='sans-serif', fontsize=12, linespacing=1. Final Report

Table of Results of Various Seed Experiments

Seed	Experimental Accuracy	F1 Score
1	0.9123	0.9087
2	0.9245	0.9201
3	0.9180	0.9142
4	0.9302	0.9265
5	0.9278	0.9239

---

Piezoelectric Sensor Simulation Summary Table

Quantity	Peak Value	Unit
Voltage (V)	±3.29 mV	mV
Force (F)	±13.14 N	N
Pressure (σ)	±0.042 MPa	MPa

Analysis of Results

• Accuracy and F1 score remained above 0.90 in all five runs (Seeds), indicating the stability of the model.

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Private user posted an update — Sep 05, 2025 09:44 PM EDT

Piezoelectric Sensor Simulation Summary Table

| Quantity | Peak Value | Unit | |---------|-------------:|---------:| | Voltage (Volt) | ±3.29 mV | mV | | Force (Fahrenheit) | ±13.14 N | N | | Pressure (σ) | ±0.042 MPa | MPa |

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Private user posted an update — Sep 05, 2025 03:08 PM EDT

Table of results of various Seed experiments

Seed	Experimental accuracy	F1 score
1	0.9123	0.9087
2	0.9245	0.9201
3	0.9180	0.9142
4	0.9302	0.9265
5	0.9278	0.9239

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Private user posted an update — Sep 05, 2025 02:59 PM EDT

Updated Comparison Table

Benchmark	Current Model	CNN	Difference (Current Model vs CNN)
Experimental Accuracy	0.9302	0.9475	−0.0173
F1 Score	0.9265	0.9408	−0.0143
AUC	0.9721	0.9834	−0.0113
Training Time	120 s	320 s	−200 s
Model Size	0.5 M	2.4 M	−1.9 M
Prediction Time	4.8 ms	12.5 ms	−7.7 ms
Energy Consumption (J/infer)	0.43 J	1.00 J	−57%

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Private user posted an update — Sep 05, 2025 02:57 PM EDT

y_true = np.repeat(range(confmat.shape[0]), confmat.sum(axis=1)) y_pred = np.repeat(range(confmat.shape[1]), confmat.sum(axis=0))

report_text = ( f"Seed Results {seed_id}:\n" f"Accuracy: {metrics.get('test_accuracy', 'N/A'):.4f}\n" f"F1 Score: {metrics.get('f1_score', 'N/A'):.4f}\n" f"Classification Report:\n{classification_report(y_true, y_pred)}" )

Training Curves

fig, ax = plt.subplots(figsize=(8, 5)) ax.plot(history['epoch'], history['accuracy'], label='Training Accuracy') ax.plot(history['epoch'], history['val_accuracy'], label='Validation Accuracy') ax.set_title(f'Training Curves – Seed {seed_id}') ax.set_xlabel('Epoch') ax.set_ylabel('Accuracy') ax.legend() pdf.savefig(fig) plt.close()

Confusion Matrix

fig, ax = plt.subplots(figsize=(6, 5)) im = ax.imshow(confmat, cmap='viridis', aspect='auto') plt.colorbar(im, ax=ax) ax.set_title(f'Confusion Matrix – Seed {seed_id}') pdf.savefig(fig) plt.close()

Text Page

fig, ax = plt.subplots(figsize=(8.5, 11)) ax.axis('off') ax.text(0, 1, report_text, va='top', family='monospace', fontsize=8) pdf.savefig(fig) plt.close()

---------- Part 2: Piezoelectric Sensor Simulation ----------

Parameters (constant values)

PARAMS = { 'd33': 2.5e-10, # C/N 'thickness_m': 0.001, # m 'spring_k': 1000, # N/m 'damping_c': 10, # Ns/m 'capacitance_C': 1e-6, # Farad 'radius': 0.01 # m }

Time and Displacement

time = np.linspace(0, 1, 500) x = 0.01 * np.sin(2 * np.pi * 5 * time) dx_dt = np.gradient(x, time)

Calculations

cross_section = np.pi * PARAMS['radius'] ** 2 F_mech = PARAMS['spring_k'] * x + PARAMS['damping_c'] * dx_dt sigma = F_mech / cross_section Q = PARAMS['d33'] * F_mech V = Q / PARAMS['capacitance_C']

Plotting

fig, axs = plt.subplots(3, 1, figsize=(10, 12), sharex=True)

axs[0].plot(time, V * 1e3) axs[0].set_ylabel('Voltage (mV)') axs[0].set_title('Piezoelectric voltage vs time') axs[0].grid(True, alpha=0.3)

axs[1].plot(time, F_mech) axs[1].set_ylabel('Force (N)') axs[1].set_title('Mechanical Force vs Time') axs[1].grid(True, alpha=0.3)

axs[2].plot(time, sigma / 1e6) axs[2].set_ylabel('Pressure (MPa)') axs[2].set_xlabel('Time (s)') axs[2].set_title('Pressure vs Time') axs[2].grid(True, alpha=0.3)

plt.tight_layout() pdf.savefig(fig) plt.close()

---------- Section 3: Innovations ----------

innovation_text = """ Model Innovations:

1. Combining a dual-hemisphere neural network with an array of intelligent piezoelectric sensors for simultaneous processing of mechanical and Electrical.
2. Physical model including spring, damping and capacitance to simulate more accurately the sensor behavior.
3. Automatic multi-run reporting mechanism combining machine learning results and sensor circuits into a comprehensive PDF file. """

fig, ax = plt.subplots(figsize=(8.5, 11)) ax.axis('off') ax.text(0.05, 0.95, innovation_text, va='top', family='sans-serif', fontsize=12, linespacing=1.5) pdf.savefig(fig) plt.close()

print(f"Final report created:

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Private user posted an update — Sep 05, 2025 02:56 PM EDT

Patent No. 140450140003002031

Inventor Nader Maleki

import os import glob import json import numpy as np import pandas as pd import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages from sklearn.metrics import classification_report from datetime import datetime

plt.style.use('seaborn-v0_8')

---------- Part 1: Multi-run network processing ----------

results_dir = "results" os.makedirs(results_dir, exist_ok=True)

Reading files by listing once

all_files = glob.glob(os.path.join(results_dir, '*')) seed_metrics = sorted([f for f in all_files if 'metrics_seed' in f and f.endswith('.json')]) seed_histories = sorted([f for f in all_files if 'history_seed' in f and f.endswith('.csv')]) seed_confmats = sorted([f for f in all_files if 'confmat_seed' in f and f.endswith('.csv')])

Create PDF

pdf_path = os.path.join(results_dir, 'summary_dual_hemisphere_2031.pdf')

with PdfPages(pdf_path) as pdf:

Title page

fig, ax = plt.subplots(figsize=(11, 8.5)) ax.axis('off') ax.text(0.5, 0.6, "Dual Hemisphere Network Report – Code 2031", fontsize=20, ha='center', fontweight='bold') ax.text(0.5, 0.4, f"Generation date: {datetime.now().strftime('%Y-%m-%d %H:%M')}", fontsize=14, ha='center') pdf.savefig(fig) plt.close()

Process each run

for metrics_file, hist_file, conf_file in zip(seed_metrics, seed_histories, seed_confmats): seed_id = os.path.splitext(os.path.basename(metrics_file))[0].split("_")[-1]

Read data

with open(metrics_file, 'r', encoding='utf-8') as f: metrics = json.load(f)

history = pd.read_csv(hist_file) confmat = pd.read_csv(conf_file, index_col=0).values

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Private user started this project — Sep 05, 2025 12:26 PM EDT

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