=Paper=
{{Paper
|id=Vol-3762/535
|storemode=property
|title=UniCas for Industry
|pdfUrl=https://ceur-ws.org/Vol-3762/535.pdf
|volume=Vol-3762
|authors=Alessio Miele,Hamza Mustafa,Michele Vitelli,Alessandro Bria,Claudio De Stefano,Francesco Fontanella,Claudio Marrocco,Mario Molinara,Alessandra Scotto di Freca
|dblpUrl=https://dblp.org/rec/conf/ital-ia/MieleMVBSFMMF24
}}
==UniCas for Industry==
https://ceur-ws.org/Vol-3762/535.pdf
UniCas for Industry
A. Miele1,† , H. Mustafa1,† , M. Vitelli1,2,*,† , A. Bria1,† , C. De Stefano1,† , F. Fontanella1,† ,
C. Marrocco1,† , M. Molinaria1,† and A. Scotto di Freca1,†
1
University of Cassino and Southern Lazio, Cassino, 03043, Italy
2
Sensichips s.r.l., 04011 Aprilia, Italy
Abstract
Artificial Intelligence (AI) is transforming industries, particularly through Industry 4.0, by integrating technologies such
as the Internet of Things (IoT) to optimize production processes and resource management. It addresses challenges such
as reducing environmental impact while fulfilling consumer demands. Innovative sensors enable real-time data collection
for environmental monitoring. Adopting advanced technologies such as energy cells, particularly lithium-ion batteries, is
crucial for sustainable mobility and reducing environmental impact in the automotive industry. It is vital to understand the
key parameters of energy cells, including range, energy density, and durability, and implement them while embracing the
principles of Second Life effectively. For example, machine learning (ML) algorithms are utilized in industrial contexts to
identify air and water pollutants and estimate the State of Charge (SoC) for automotive applications. These methodologies
improve efficiency, sustainability, and innovation in various industrial sectors.
Keywords
Artificial Intelligence, Industry 4.0, AI on the Edge, Smart Sensors, Pollutants Identification, State of Charge estimation
1. Introduction terpret sensor data offers numerous benefits. By utiliz-
ing sophisticated algorithms and predictive models, it is
Artificial Intelligence (AI) is revolutionizing various sec- possible to identify pollutants accurately, continuously
tors including healthcare, finance, education, transporta- monitor air and water quality, and optimize industrial
tion, and notably, industry. Its capacity to analyze vast processes to reduce environmental impacts. However,
data sets in real-time and generate precise predictive the applications of artificial intelligence in industry are
insights is reshaping production processes, enhancing not limited to the environmental sphere. Nowadays, the
resource allocation, and boosting operational efficiency automotive industry is facing one of the most significant
in industries worldwide. Industry 4.0 [1] represents a challenges in its history: to provide sustainable mobility
crucial turning point in the evolution of the industrial and reduce the environmental footprint of transportation
landscape, characterized by the integration of advanced on a global scale. One of the key pillars of this trans-
technologies and widespread digitization of production formation is the energy cell[7]. Energy cells, notably
processes. Inspired by the notion of the "smart factory," lithium-ion batteries, are crucial in revolutionizing vehi-
it emphasizes the interconnection of machines, systems, cle energy usage towards zero-emission transportation,
and people via IoT, AI, big data, cloud computing, and combating air pollution, and mitigating climate change.
advanced robotics[2, 3, 4]. The concept of Industry 4.0 is However, to fully realize it is essential to have a precise
based on the idea of automated and connected production, comprehension of key parameters like range, energy den-
where machines and systems communicate with each sity, charging time, and durability. Accurate estimation
other in real-time to optimize processes and decision- of these parameters is critical for developing large-scale
making. In this context, innovative sensors [5, 6] play a zero-emission vehicles and ensuring proper disposal and
crucial role by enabling the collection of detailed, real- reuse, aligning with Second Life principles[8].
time data on various environmental and operational pa- The following sections highlight the application of Ma-
rameters. Using artificial intelligence to analyze and in- chine Learning (ML) in industrial challenges, focusing
on the detection of pollutants in air (2) and water (3), and
Ital-IA 2024: 4th National Conference on Artificial Intelligence, orga- on State of Charge estimation in automotive applications
nized by CINI, May 29-30, 2024, Naples, Italy (4).
*
Corresponding author.
†
These authors contributed equally.
$ alessio.miele@unicas.it (A. Miele); hamza.mustafa@unicas.it 2. Pollutant Identification in Air
(H. Mustafa); michele.vitelli@unicas.it (M. Vitelli); a.bria@unicas.it
(A. Bria); destefano@unicas.it (C. De Stefano); fontanella@unicas.it Our recent study proposes a novel system integrating
(F. Fontanella); c.marrocco@unicas.it (C. Marrocco); sensor technology and machine learning to detect and
m.molinara@unicas.it (M. Molinaria); a.scotto@unicas.it (A. Scotto
di Freca) classify air contaminants effectively and affordably. Cur-
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License rent monitoring solutions face size, cost, and complexity
Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
issues[9], prompting the development of a more acces- DATA ACQUISITION
sible solution. Challenges like low sensitivity and se- Software: SENSIPLUS API
Hardware: MCU and/or Host
lectivity in miniaturized, low-cost smart solutions are
addressed, with comparisons made to CEN (European
Committee for Standardization) reference instruments, PREPROCESSING
noting lower accuracy and stability but highlighting their Software: EMA filtering and
normalization
value in data aggregation[10]. Spatial analysis techniques Hardware: MCU and/or Host
aid in evaluating pollutant sources, while the limitations
of chemical micro-sensors are extensively discussed in
CLASSIFICATION
the literature, along with methodologies to improve their
Software: MLP, CNN or LSTM
performance. Hardware: MCU or Host
At the core of our proposed system is a sensor array
including aluminum oxide for broad-spectrum volatile Figure 2: The proposed integrated system. SDM stands for
SENSIPLUS Deep Machine.
organic compound detection, a commercial capacitive
humidity sensor, and graphene-functionalized sensor for
pollutant sensitivity. These selections aim for versatil-
ity and sensitivity across contaminant types. Integration This includes phases of baseline air exposure, controlled
with the SENSIPLUS platform facilitates precise electrical introduction of contaminants, and subsequent recovery,
impedance measurements, crucial for accurate air quality designed to capture the dynamic nature of indoor air
assessment. The proposed integrated system is shown quality. This methodological approach is crucial for pro-
in Figure 1 and is mainly composed of the following: (1) ducing comprehensive sensor data that reflects the com-
SENSIPLUS Chip (henceforth SPC): a microelectronic plexities of real-world indoor environments.
measurement device with on-chip sensing capabilities, For the analytical component of our study, we im-
jointly developed by Sensichips s.r.l.[11] and the Depart- plemented several machine learning models, including
ment of Information Engineering at the University of Multi-Layer Perceptrons (MLP), Convolutional Neural
Pisa. Equipped with a versatile analog front end and Networks (CNN), and Long Short-Term Memory (LSTM)
various internal and external ports, it enables electrical networks. These models were trained on datasets col-
impedance measurements on both internal and exter- lected from our sensor array, to achieve high accuracy in
nal sensors. It has already been adopted in other works, classifying different air contaminants. The contaminants
as in [12, 13, 14]. (2) SENSIPLUS Deep Machine (SDM): included in our study encompass a range of substances
a hardware/software module designed for data acqui- commonly found in indoor settings, such as acetone, al-
sition, processing, and analysis. The block diagram in cohol, ammonia, bleach, and various volatile organic
Figure 2 illustrates the logical flow of operations, high- compounds (VOCs), along with controls like water vapor
lighting the software and hardware components utilized and clean air to facilitate accurate classification between
for each task. Data acquisition is enabled by the SPC API, polluted and unpolluted conditions.
a software library in C or Java, operating respectively Our findings indicate that the system can classify air
on Micro Controller Units (MCUs) and multiple hosts contaminants with an average accuracy surpassing 75%,
like Linux/Windows/Android, depending on application showcasing its potential effectiveness in indoor air qual-
needs. Classification tasks utilize ML techniques like ity assessment. However, classification accuracy varied
MLP, CNN, or LSTM, adaptable to run on MCU or more among different contaminants, with notable challenges
powerful devices like PCs, depending on computational in distinguishing similar substances like acetone and al-
requirements. cohol. This variation underscores the complexities of
air quality monitoring and identifies avenues for future
enhancement.
In evaluating the system’s operational efficiency, we
prioritized minimizing data acquisition times and en-
ergy consumption, optimizing for low-power operations
ideal for IoT applications. This focus ensures the effec-
tiveness and practicality of our solution for real-world
deployment, highlighting the importance of efficiency in
Figure 1: The proposed integrated system. SDM stands for environmental monitoring technologies.
SENSIPLUS Deep Machine. Looking forward, we anticipate several potential en-
hancements to our system. These include integrating
Our methodology involves a structured measurement additional sensor types to expand the range of detectable
protocol to simulate various indoor air quality conditions. contaminants, exploring advanced machine learning
SENSIPLUS [15]. The system’s objective is to detect sub-
stances in wastewater. However, direct measurements
from sewage drains are impractical due to unreliable con-
ditions and health risks. To address this challenge, Syn-
thetic WasteWater (SWW) is created to simulate sewage
composition. The recipe used to create SWW is inspired
by previous work, and pH adjustments are made to repli-
cate real wastewater conditions. Fourteen substances
have been spilled in the SWW background: (1) Acetic
Acid; (2) Acetone; (3) Ethanol; (4) Ammonia; (5) Formic
Acid; (6) Phosphoric Acid; (7) Sulphuric Acid; (8) Hydro-
gen Peroxide; (9) Synthetic Waste Water; (10) Sodium
Hypochlorite; (11) Sodium Chloride; (12) Dish Wash De-
tergent; (13) Wash Machine Detergent; (14) Nelsen. These
substances are divided into two categories: substances to
be identified (group 1) and outlier samples (group 2) to be
excluded by the system. This method guarantees a safe
Figure 3: CNN Global Confusion Matrix. environment for dataset creation without any biological
risks.
models to enhance classification accuracy, and develop-
ing expanded real-time monitoring capabilities. These
efforts aim to further improve the comprehensiveness
and usability of our indoor air quality monitoring system.
In conclusion, our work contributes to environmental
monitoring efforts by demonstrating the feasibility of a
sensor-based and machine-learning-integrated system Figure 4: Identification system architecture.
for indoor air quality assessment. While promising, our
results also highlight the challenges in air quality moni-
toring and the necessity for continued innovation in this To enhance sensitivity to the substances of interest
field. Our study represents a step toward achieving more and the RedOx dynamics, the six IDEs of the SCW were
accessible, efficient, and accurate air quality monitoringcoated with six different metals: Gold (M1), Copper (M2),
solutions. Silver (M3), Nickel (M4), Palladium (M5), and Platinum
(M6). From the resulting sensors, we recorded the resis-
tance measured at a frequency of 78 kHz for the Gold and
3. Pollutant Identification in Water Platinum IDEs, while Resistance and Capacitance were
measured at a frequency of 200 Hz for Gold, Platinum,
Detecting illegal pollutants in wastewater is crucial for Silver, and Nickel. This yielded a feature vector com-
public health and security. An End-to-End IoT-ready prising ten values: six resistance and four capacitance
node is proposed for sensing, processing, and transmit- measurements. Notably, the experimental campaign did
ting wastewater pollutant data. Utilizing Smart Cable not involve the use of Palladium and Copper IDEs.
Water with SENSIPLUS chip sensors, the system employs
impedance spectroscopy to distinguish pollutants from
other substances. Data processing, on a low-cost Micro 3.2. Classification
Control Unit, involves anomaly detection, classification, The classification system consists of two phases: Data
and false positive reduction through machine learning Preprocessing and Classification. In the Data Preprocess-
algorithms. ing phase, raw sensor data is normalized before being
sorted and evaluated by a Finite State Machine (FSM)
3.1. Metodology shown in Figure 5. This process determines whether the
data should proceed to the Classification phase.
The identification system, depicted in Figure 4, utilizes The Data Preprocessing phase involves normalizing
the Smart Cable Water (SCW), an IoT-ready smart sensor the raw data from sensors, establishing a robust baseline
system developed by Sensichips s.r.l. The SCW com- signal, and determining whether the normalized sample
prises InterDigitated Electrodes (IDEs) and is based on should proceed to the anomaly detector or be directly
classified using the FSM.
4. Optimization of Battery State of
Charge Estimation
Accurate monitoring of State of Charge (SoC) is crucial
for tasks like battery life estimation and temperature
control. Existing techniques like Coulomb counting and
Open Circuit Voltage (OCV) face challenges such as mea-
surement errors and the flat relationship between voltage
and SoC in certain battery types like Lithium Iron Phos-
phate (LFP). Electrochemical Impedance Spectroscopy
(EIS) emerges as a promising alternative but suffers from
Figure 5: Identification system architecture.
long measurement times. This work proposes a method
to minimize measurement time while ensuring accurate
SoC estimation, particularly with EIS and knowledge-
In real scenarios, distinguishing between substances based SoC classes.
of interest and others in the sewerage network is cru- The proposed approach follows the framework shown
cial. The primary aim is to determine if the substance in Figure 1. It starts with the identifying design parame-
being investigated is of interest, minimizing subjective ters and constraints, which include: (1) Resolution of SoC
evaluations unless specified. performance estimation, (2) Target measurement time,
The identification phase involves anomaly detection (3) Target Accuracy, (4) Battery type, and (5) Classifier.
and multiclass classification for precise substance identifi- The second step is to characterize the device under test,
cation. Anomaly detection excludes common substances,
focusing on outliers, while classification employs opti- Constrains Definition
mized KNN models trained solely on samples of inter-
est. Grid search methods enhance the accuracy of both Characterization of
batteries
anomaly detection and multiclass classification models. under test
NO
Is the classifier fixed?
3.3. Results
Classifier
The study combined anomaly detection with a multi-
YES selection
class classifier for the final test, as illustrated in Figure 6.
However, the multiclass classifier incorrectly identified Feature Selection
some outlier substances, leading to false positive alarms.
To mitigate this, the anomaly detection system was in-
tegrated before the multiclass classifier. Consequently,
Selected Frequencies
most outlier samples were accurately classified as ’UN-
KNOWN,’ achieving an accuracy rate of 79.4%. Notably, Figure 7: The proposed method workflow.
20.6% of outlier samples, primarily sodium hypochlorite,
were frequently misclassified as hydrogen peroxide. focusing on achieving the most stringent parameters pos-
sible. Then the appropriate classifier from the previous
dataset is evaluated. The chosen classifier, demonstrating
better accuracy, is then integrated into the feature selec-
tion algorithm. The final stage involves feature selection
using search algorithms, aimed at minimizing measure-
ment time while preserving accuracy above the specified
target.
4.1. Metodology
In this example, the State of Charge (SoC) estimation
problem was addressed using 10-class classification mod-
els where each class represents a 10% interval of the SoC.
Figure 6: Entire system results shown as Confusion Matrix.
The initial dataset comprises all available features, includ-
ing 28 impedances (real and imaginary parts) measured
at various frequencies, totaling 56 features collected from
7 different cells. These features represent the Nyquist
plots of battery impedances at different SoCs, as illus- 1.0
trated in Figure 8. Performance evaluation metrics used
10 0.90 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.09 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
are described by Grandini et al[16]. The experiments 20 0.06 0.88 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.10 0.12 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.8
30 0.00 0.00
0.00 0.88 0.12 0.00 0.00 0.00 0.00 0.00 0.00
0.00 0.07 0.07 0.00 0.00 0.00 0.00 0.00 0.00
40 0.00 0.00 0.06 0.83 0.10 0.00 0.00 0.00 0.00 0.00
0.00 0.00 0.10 0.06 0.05 0.00 0.00 0.00 0.00 0.00
251.2
0.6
50 0.00 0.00
0.00 0.00 0.06 0.81 0.12 0.00 0.00 0.00 0.00
0.00 0.00 0.06 0.10 0.07 0.00 0.00 0.00 0.00
TRUE
60 0.00 0.00 0.00 0.00 0.06 0.71 0.17 0.06 0.00 0.00
63.1 SoC = 100 %
0.00 0.00 0.00 0.00 0.10 0.12 0.14 0.06 0.00 0.00
SoC = 95 %
SoC = 85 %
0.4
70 0.00 0.00
0.00 0.00 0.00 0.00 0.12 0.79 0.08 0.00 0.00
SoC = 75 %
15.8 SoC = 65 %
SoC = 55 % 0.00 0.00 0.00 0.00 0.14 0.21 0.09 0.00 0.00
80 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.85 0.04 0.00
SoC = 45 %
SoC = 35 % 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.09 0.06 0.00
4.0 SoC = 25 % 0.2
90 0.00 0.00
0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.83 0.06
SoC = 15 %
SoC = 5 % 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.06 0.06
0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.94
100 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.06
44.1 62.5 88.7 125.7 178.3 252.7
0.0
10
20
30
40
50
60
70
80
90
0
Figure 8: Obtained Nyquist plot of a single battery at different
10
State of Charge. PREDICTED
consistently followed the k-fold method, ensuring opti- Figure 9: Obtained confusion matrix of the Support Vector
mal dataset utilization by rotating the batteries used. As Machine model, with mean value (top) and standard deviation
a result, six trained models were obtained. Evaluation (bottom) for each class.
metrics confirm that the Support Vector Machine (SVM)
model outperforms others, with a mean accuracy of 0.83
and a standard deviation of 0.04. The resulting confusion imum Accuracy achieved over 50 runs, correlating with
matrix shown in Figure 9 illustrates the performance of the weight coefficient, while considering measurement
the SVM model. These preliminary classification tests time. The blue star indicates the solution with the highest
identify SVM as the most effective ML model among Accuracy. The band represents SoC estimation Accuracy
those considered. considering all features with the SVM classifier,showing
The problem of identifying the optimal set of frequen- a trade-off between accuracy and measurement time opti-
cies for impedance measurement via EIS for battery SoC mization, where higher 𝛼 values prioritize accuracy over
estimation has been addressed using optimization algo- time.
rithms as search strategies, specifically Particle Swarm
Optimization (PSO) [17]. A fitness function is imple- 0 D [ L P X P $ F F X U D F \
$ F F X U D F \ I R U P L Q L P X P P H D V X U H P H W W L P H
0 H D V X U H P H Q W W L P H I R U W K H P D [ L P X P $ F F X U D F \
0 L Q L P X P P H D V X U H P H W W L P H
mented based on a supervised learning model 1, aiming
to balance accuracy in SoC estimation and measurement
$ F F X U D F \ Z L W K D O O I H D W X U H V
time. The parameter 2 represents the ratio of correct pre- 1 R U P D O L ] H G P H D V X U H P H Q W W L P H
$ F F X U D F \
dictions (CP) to total predictions (TP), while parameter 3
is inversely related to measurement duration. Measure-
ment time (𝑇𝑚𝑒𝑎𝑠 ) is computed as the sum of selected
feature durations, with 𝑇𝑚𝑎𝑥 related to the use of all
features. Parameters 𝐴 and 𝐵 range from 0 to 1, with
Z H L J K W F R H I I F L H Q W
𝛼 serving as a weight coefficient between accuracy and Figure 10: Accuracy and measurement time as a function of
time contributions. weight coefficient for PSO+SVM combination.
𝑆 = 𝛼 · 𝐴 + (1 − 𝛼) · 𝐵 (1)
𝐶𝑃
𝐴= (2)
𝑇𝑃 Acknowledgments
𝑇𝑚𝑒𝑎𝑠
𝐵 =1− (3)
𝑇𝑚𝑎𝑥 The EU partially supported this work in the NextGenera-
tionEU plan through MUR Decree n. 1051 23.06.2022
4.2. Results "PNRR Missione 4 Componente 2 Investimento 1.5" -
CUP H33C22000420001, partially through Horizon Eu-
This case study establishes a target accuracy of 0.95, re-
rope RHE-MEDiation with (GA 101113045).
gardless of measurement time. Figure 10 shows the max-
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