Ability Of System Research Articles

Development of Efficient Models of Artificial Intelligence for Autonomous Decision Making in Dynamic Information Systems

This research paper focuses on the development of efficient artificial intelligence models for autonomous decision-making in dynamic information systems. Using innovative approaches in data analysis and algorithm optimization, we explore ways to improve model performance in dynamic environments. The results of this research can provide a deeper understanding of how artificial intelligence can operate effectively in real time, thus opening up new perspectives for application in different industries. The research includes the implementation of advanced machine learning techniques, as well as the analysis of adaptive models that can adapt to changes in the environment. Key attention is devoted to the optimization of resources in order to ensure quick and precise decision-making in dynamic situations. In addition, the work addresses the integration of the model with high-performance sensors to improve the system's ability to gather relevant information for decision-making. Through this interdisciplinary analysis, we aim to contribute to the development of intelligent systems that can autonomously react to changes and unforeseen situations in real time.

Smart co-delivery of plasmid DNA and doxorubicin using MCM-chitosan-PEG polymerization functionalized with MUC-1 aptamer against breast cancer

This study introduces an innovative co-delivery approach using the MCM-co-polymerized nanosystem, integrating chitosan and polyethylene glycol, and targeted by the MUC-1 aptamer (MCM@CS@PEG-APT). This system enables simultaneous delivery of the GFP plasmid and doxorubicin (DOX). The synthesis of the nanosystem was thoroughly characterized at each step, including FTIR, XRD, BET, DLS, FE-SEM, and HRTEM analyses. The impact of individual polymers (chitosan and PEG) on payload retardation was compared to the co-polymerized MCM@CS@PEG conjugation. Furthermore, the DOX release mechanism was investigated using various kinetic models. The nanosystem's potential for delivering GFP plasmid and DOX separately and simultaneously was assessed through fluorescence microscopy and flow cytometry. The co-polymerized nanosystem exhibited superior payload entrapment (1:100 ratio of Plasmid:NPs) compared to separately polymer-coated counterparts (1:640 ratio of Plasmid:NPs). Besides, the presence of pH-sensitive chitosan creates a smart nanosystem for efficient DOX and GFP plasmid delivery into tumor cells, along with a Higuchi model pattern for drug release. Toxicity assessments against breast tumor cells also indicated reduced off-target effects compared to pure DOX, introducing it as a promising candidate for targeted cancer therapy. Cellular uptake findings demonstrated the nanosystem's ability to deliver GFP plasmid and DOX separately into MCF-7 cells, with rates of 32% and 98%, respectively. Flow cytometry results confirmed efficient co-delivery, with 42.7% of cells showing the presence of both GFP-plasmid and DOX, while 52.2% exclusively contained DOX. Overall, our study explores the co-delivery potential of the MCM@CS@PEG-APT nanosystem in breast cancer therapy. This system's ability to co-deliver multiple agents preciselyopens new avenues for targeted therapeutic strategies.

Phosphorus recovery from sewage sludge via Mg-air battery system

A pressing issue in contemporary society is the resource scarcity of phosphorus. Operating on the principle of electrochemical reactions between Mg as the anode and oxygen from air as the cathode, Mg-air batteries (MAB) have been employed to provide new prospects for phosphorus recovery in struvite form. Different phosphorus concentrations and reaction time impact struvite generation in MAB systems; however, the exact mechanism has rarely been investigated. We investigated how varying the initial phosphorus concentration and the reaction time affects phosphorus recovery, electricity generation, and the efficiency of struvite production in MAB. Additionally, we examine the impact of solid carbon sources on phosphorus transformation in sludge. The findings revealed that the incorporation of solid carbon sources facilitated the release of phosphate by changing phosphorus speciation. The electrolyte derived from the conditioned sludge filtrate exhibited a remarkable phosphorus removal efficiency of 91.7 % within 1 h, yielding the highest struvite purity of ∼70 %, whereas that using raw sludge filtrate or extending the reaction time was found to be less effective, even reducing struvite formation. Furthermore, different electrolytes influence the system's ability to passivate anode, and electrolytes with higher phosphorus concentrations have better electricity production performance. The results by Visual MINTEQ model confirmed that longer reaction times and lower initial phosphorus concentrations can negatively affect struvite formation by introducing Mg3(PO4)2 and Mg(OH)2. The integration of agricultural waste as carbon sources with MAB for phosphorus recovery represents a potential methodology for struvite recuperation from sewage sludge, thereby heralding a sustainable strategy for resource recovery.

IoT based Remote Patient Monitoring System

Remote Patient Monitoring (RPM) systems have emerged as transformative tools in healthcare, leveraging technological advancements to enhance patient care beyond traditional clinical settings. This abstract explores the design and implementation of a Remote Patient Monitoring System aimed at bridging the gap between patients and healthcare providers. The system employs a network of wearable devices and sensors to continuously collect and transmit relevant patient data to a secure cloud-based platform. Through real- time monitoring, healthcare professionals can remotely assess vital signs, medication adherence, and other crucial health metrics, enabling early intervention and personalized care. The integration of data analytics and machine learning algorithms further enhances the system's ability to detect patterns, predict potential health issues, and optimize treatment plans. This abstract delves into the key features, benefits, and challenges associated with the Remote Patient Monitoring System, emphasizing its potential to revolutionize healthcare delivery by fostering proactive, patient-centered care and improving overall health outcomes.

Development of a Multimedia-Based Psychological Education Assessment System for Higher Education Institutions

Mental health education in colleges and universities has made considerable progress, but the existing assessment model still faces challenges in terms of time overhead and rank indicators. In response, this paper proposes a new psychological education assessment model for colleges and universities, based on multimedia feature extraction techniques. The proposed model utilizes word vectorization with Word2vec and an improved transformer network, incorporating deeply separable convolutions and an LSTM network to establish long-range dependency coding. Experimental results show that the proposed model outperforms traditional feature extraction methods in terms of extraction speed, feature readability, and model efficiency. Furthermore, the study suggests that the use of reinforcement learning can improve the system's ability to capture key concepts and enhance accuracy. The proposed approach has significant implications for improving mental health education in colleges and universities and can be applied to similar professional environments.

An Automated Insulin Delivery System with Automatic Meal Bolus Based on a Hand-Gesturing Algorithm.

Background: Carbohydrate counting (CC) and meal announcements, before eating, introduce a significant burden for individuals managing type 1 diabetes (T1D). An automated insulin delivery system with automatic bolusing that eliminates the need for CC and premeal bolusing (i.e., a hands-free closed-loop [HFCL] system) was assessed in a feasibility trial of adults with T1D. Methods: The system included the MiniMed™ 780G pump and a smartphone-paired smartwatch with the Klue application (Klue, Inc.) that detects eating and drinking gestures. A smartphone algorithm converted gestures into carb amounts that were transmitted to the pump for automatic bolusing. For 5 days, participants (N = 17, 18-75 years of age) used the system at home with meal announcements based on traditional CC, with the Klue application disabled (Home-stay phase). Thereafter, participants moved to a supervised hotel setting, where the Klue application was enabled for 5 days and meals were not announced (Hotel-stay phase). Participants consumed the same eight test meals (six solid and two liquid) of varying caloric and carb size at the same time and day of the week for both phases, and glycemic metrics were compared. Otherwise, there were no other meal restrictions. Results: The overall time in range (70-180 mg/dL) was 83.4% ± 7.0% and 80.6% ± 6.7% for the Home-stay and Hotel-stay, respectively (P = 0.08). The average time at <70 mg/dL was 3.1% and 3.0% (P = 0.9144), respectively, and the average time at >180 mg/dL was 13.5% and 16.3% (P = 0.1046), respectively. Postprandial glycemia following low-carb test meals was similar between the two phases. The system's ability to accommodate high-carb meals was somewhat limited. There were no episodes of severe hypoglycemia or diabetic ketoacidosis. Conclusion: Preliminary findings show that a HFCL system was safe and maintained overall glycemic control, similar to that observed with traditional CC and manual meal bolusing. By eliminating these daily T1D burdens, a HFCL system may improve quality of life for individuals with T1D. ClinicalTrials.gov number: NCT04964128.

Interferon Lambda: An Anonymous Warrior of Host-Microbial Fray in The Pathogenesis of Periodontal Diseases

The current body of research focuses primarily on three fundamental groups of interferons: alpha, beta, and gamma interferons. However, little attention is paid to another important group of interferons called lambda interferons (IFN-λs), which have demonstrated activity against periodontal pathogens, particularly viruses. These recently discovered interferon-lambdas provide a striking contrast to the periodontal pathogens, exerting impact both at the capacity of the immune system and the cell, thereby affecting a variety of inflammatory and response pathways. Thus, this review sheds light on role of interferon lambda in the development of periodontal disease and associated activities. In order to collect the information necessary for this review, an exhaustive search of the relevant literature was conducted. The review focused on the structure, regulatory mechanisms, and role of interferon lambda in medical and periodontal infections and diseases. The findings of our review indicated that IFN-λs have an effect on the immunological regulatory mechanisms of the host in relation to viruses, bacteria, fungi, and parasites; nevertheless, their primary role is antiviral, and they have little to no effect on the immune system's ability to fight bacteria. Bangladesh Journal of Medical Science Vol.23 (Special Issue) 2024 p.S73-S78

Revolutionizing Wellness: The IoT Smart Bottle for Health Monitoring

Intelligent and health-focusedIoT project designed to monitor and provide information about the contents of a bottle, such as temperature and water level. This system utilizes a DHT11 sensor for temperature monitoring, an ultrasonic sensor for water level detection, and an LCD to display real-time information. The project aims to promote health awareness by allowing users to monitor the temperature of liquids and ensure anadequate water supply in their bottles. The smart bottle's sensors detect when the medication is dispensed, and thedata is transmitted to a centralized platform via a secure IoT network. This not only assists healthcare professionals in tracking patient compliance but also facilitates personalized healthcare by enabling timely adjustments to treatment plans. With the ability to send alerts and notifications to both patients and healthcare providers, the Smart Bottle Health Care using IoT enhances patient engagement, fosters medication adherence, and ultimately contributes to better health outcomes. This innovative solution holds great potential in transforming medication management, particularly for individuals with chronic conditions who require consistent and precise dosage adherence for effective treatment. Through continuous monitoring and data analysis, the IoT-enabled smart bottle not only ensures timely medication reminders but also provides a holistic view of patients' medication adherence trends. This data-driven approach empowers healthcare providers to make informed decisions, intervene proactively, and tailor treatment plans for improved patient outcomes. The system's ability to send notifications and alerts fosters a collaborative healthcare ecosystem, engaging both patients and caregivers inthe medication

Understanding complex processes better-A case study on increasing patient safety and efficiency in a central operating room

Various professional groups are involved in the daily work of the central operating room with the aim of providing the best possible treatment for each individual using modern medical technology (sociotechnical system) in acost-effective manner. Ensuring perioperative patient safety is of particular importance. At the same time, the efficient use of the central operating room is essential for the economic success of ahospital. Preoperative preparation is acomplex process with many substeps that are often difficult to manage. Historically, the focus has been on retrospective learning from errors and incidents. More recent approaches take asystemic view. Acentral idea is to consider the mostly positive course of treatment and the adjustments to daily work that are currently required by the people involved (Safety-II). By taking greater account of how the many components of the system interact, processes can be better understood and specific measures derived. This strengthens the system's ability to adapt to changes and disturbances, thus ensuring that goals are achieved. The functional resonance analysis method (FRAM) is an internationally recognized method for modelling work as done compared to work as imagined. This paper presents the application of FRAM to preoperative preparation in a major regional hospital. Is FRAM suitable for improving process understanding in preoperative preparation? An interdisciplinary project team identified relevant functions of preoperative preparation through document analysis and walkthroughs. Based on this, more than 30guided interviews were conducted with functionaries. The results were presented graphically and specific information, such as safety-related statements or reasons for the variability of functions, were also presented textually. In the next phase, statements were evaluated and compared with the target model and the job descriptions. The FRAM revealed the process as acomplex network of relationships. During the modelling process, avarying degree of centrality and variability of certain functions became apparent. From the observations, the project team selected those with high relevance for patient safety and for the efficiency of the overall process in order to prioritize starting points for deriving measures to increase resilience. These starting points relate either to single functions, such as surgical site marking or to multiple functions that are variable in their execution, such as delays due to nonsynchronized duty times. The FRAM conducted provides valuable new insights into the functioning of complex sociotechnical systems that go far beyond classical linear methods. The awareness of operational processes gained and the resulting dynamic view of interactions within the system enable specific measures to be derived that promote resilient behavior and reduce critical variability, thus contributing to increased patient safety and efficiency.

Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are essential in many areas, from healthcare to environmental monitoring. However, WSNs are vulnerable to routing attacks that might jeopardize network performance and data integrity due to their inherent vulnerabilities. This work suggests a unique method for enhancing WSN security through the detection of routing threats using feed-forward artificial neural networks (ANNs). The proposed solution makes use of ANNs' learning capabilities to model the network's dynamic behavior and recognize routing attacks like black-hole, gray-hole, and wormhole attacks. CICIDS2017 is a heterogeneous dataset that was used to train and test the proposed system in order to guarantee its robustness and adaptability. The system's ability to recognize both known and novel attack patterns enhances its efficacy in real-world deployment. Experimental assessments using an NS2 simulator show how well the proposed method works to improve routing protocol security. The proposed system's performance was assessed using a confusion matrix. The simulation and analysis demonstrated how much better the proposed system performs compared to the existing methods for routing attack detection. With an average detection rate of 99.21% and a high accuracy of 99.49%, the proposed system minimizes the rate of false positives. The study advances secure communication in WSNs and provides a reliable means of protecting sensitive data in resource-constrained settings.

Design and Development of Automated Control of Restaurant Robot

This paper presents a novel voice-controlled restaurant robot system designed to revolutionize the dining experience by seamlessly integrating advanced robotics and natural language processing technologies. The proposed system aims to enhance customer service, operational efficiency, and overall dining satisfaction by providing a user-friendly and interactive platform. .The restaurant robot is equipped with a comprehensive set of capabilities, including, voice recognition and manipulation. By leveraging voice control, users can interact with the robot using natural language commands, enabling seamless communication and intuitive control. This allows customers to effortlessly place orders The system's is comprises a mobile robotic platform, bluetooth modules, a cloud-based natural language processing, The voice control functionality is implemented through a sophisticated speech recognition system, enabling the robot to accurately understand and interpret user commands. .In addition to customer-facing interactions, the restaurant robot also assists restaurant staff in various tasks. Deliver food and beverages to tables, handle payment transactions, and perform routine cleaning tasks. By automating these processes, the system improves operational efficiency, reduces labor costs, and allows human staff to focus on highervalue activities such as customer engagement and culinary expertise. .To evaluate the performance and usability of the voice-controlled restaurant robot, a series of experiments were conducted in a real-world restaurant environment. The results demonstrated the system's high recognition accuracy, robustness in noisy environments, and its ability to effectively handle a wide range of user requests. .The proposed voice-controlled restaurant robot presents a promising solution for transforming the dining experience. It combines the convenience of voice control with advanced robotics, enabling seamless and interactive interactions between customers, staff, and the robotic platform. The system's ability to enhance customer service, operational efficiency, and overall dining satisfaction positions it as a significant advancement in the hospitality industry.

Enhancing Students' Preparation for Math Exams using the Advanced Features of the Halomda Platform

This paper highlights the new enhancements of the Halomda platform designed to improve students' learning and exam preparation. The new features include a Graph Plotter, Algebraic Calculator and Context ChatGPT Recommended Key Guidelines. The use of these features is demonstrated though a working example of solving a double integral, illustrating how students' self-preparation for mathematics exams is supported by interactive context feedback. In addition, the system's ability to check intermediate steps of a solution, complemented by graphing, comparison of algebraic equivalence of algebraic operations, and guided AI bot assistance, contributes to an enriched learning experience and improved self-assessment for students. In case of insufficient understanding of the solution, the AI chat can be consulted for further clarification. Since the solution in the "Help" mode is developed by the teacher who prepared the test paper, in case of misunderstanding, the student can turn to external sources, and the fastest way is the AI chat.

A Review on Use of Emulsified Acids for Matrix Acidizing in Carbonate Reservoirs.

Almost 60% of oil and 40% of gas reserves worldwide are contained in carbonate reservoirs where acidizing stimulation is more challenging compared to sandstone reservoirs. Utilization of emulsified acids in matrix acidizing operations has been the most effective technique for more than half a century. This is due to the colloidal system's ability to generate deep, narrow conduits toward production zones under controlled retarded reactivity with the rock surface, along with the excellent sweep efficiency and corrosion inhibition of the well equipment. This Review attempts to review the various kinds of emulsified acids that are used for matrix acidizing of carbonate formations. The Review is trying to trace the innovations that have, gradually, been applied for enhancing the performance of emulsified acids for a variety of conditions, their limitations, and the developmental solutions such hybrid emulsifiers and pickering emulsions. In addition, the Review also discusses the parameters, characteristics, and measuring techniques required for the successful synthesis of a stable and quality emulsion while considering the environmental concerns raised toward the application of an emulsified acid system. From the reviewed publications, it can be summarized that macroemulsions are best suited for matrix acidizing applications over microemulsions due to low emulsifier concentrations and high acid volume retention; similarly, water in oil emulsions provide better retardation in a comparison to oil in water emulsions. The small droplet size of the emulsion yields high viscosity and stability. The compositional balance between each component present in the acidizing system is a crucial factor for optimum performance. Moreover, for future practice, much focus is required to design emulsified acids as ecofriendly systems that can leave the least amount of toxicity during and after implementation.

Feasibility of integrated solar photovoltaic pico‐pumped storage self‐sustained system for multi‐storey building in India

AbstractThis study explores the potential of utilizing a pico‐pumped storage system (PPSH) as an energy storage solution to enhance the integration of renewable energy sources in a multi‐story building. The study seeks to evaluate the system's ability to fulfil the energy requirements of a part of a multi‐storey building, considering factors such as cost‐effectiveness, longevity, and environmental impact. Basic relationships are used to model both floating solar PV systems and pumped storage systems in MATLAB. An algorithm based on linear programming is used to generate the optimal schedule, while a particle swarm optimization‐based algorithm is employed for the optimal design of the proposed system. In order to analyze the economic feasibility, the levelized cost of solar energy and the levelized cost of stored energy are estimated. An optimally sized floating solar PV integrated pico pumped storage system with an optimal operating strategy can perform technically. However, due to practical limitations, the system's dimensions cannot accommodate real‐world storage requirements. When levelized costs are considered, a pico‐pumped storage system becomes less appealing than battery storage systems, particularly if a new storage tank is to be constructed. Compared to battery storage systems, pumped storage systems have a lower energy density, which requires more space and height, especially in buildings. Pumped storage systems, on the other hand, have a longer lifespan and a lower environmental impact. Overall, the proposed system, along with an optimal scheduling algorithm, provides a long‐term solution for energy storage, aligning with the growing global emphasis on lowering carbon emissions and embracing renewable energy sources. Although there may be a high initial investment requirement and a restricted storage capacity, the sustained advantages of energy independence and a diminished carbon footprint render it an appealing option for environmentally conscious communities and green buildings.

COMPARISON BETWEEN SMF AND SPSW STEEL BUILDING FOR MIXED-USE RESIDENCE USING THE AHP METHOD

The objective of this work is to compare the Special Moment Frame (SMF) system and the Steel Plate Shear Wall (SPSW) system using the AHP multi-criteria comparison method with the two Seismic Load Resisting Systems (SFRS). From the linear static analysis, dynamic analysis, and nonlinear static analysis (Pushover), representative characteristics of each Seismic Force-Resisting System (SFRS) are determined. These characteristics include the structure's self-weight, target displacement, floor drifts, and seismic response modification factor (R). As a result, two buildings are obtained with different SFRS that comply with the design of their elements and checks according to the regulatory requirements. After determining the capacity curve of each Seismic Force-Resisting System (SFRS) and assessing the performance level of the buildings, the conclusion is drawn that the Special Plate Shear Wall (SPSW) system is more suitable and advantageous compared to the Special Moment Frame (SMF) system. This preference is due to the SPSW system's ability to provide ductility, which translates into higher energy dissipation capacity, and stiffness, which leads to reduced floor drifts in the building.

Design of fault tolerant algorithm for network on chip router using field programmable gate array

&lt;p class="Abstract"&gt;Many internet protocol (IP) modules are present in contemporary system on chips (SoCs). This could provide an issue with interconnection among different IP modules, which would limit the system's ability to scale. Traditional bus-based SoC architectures have a connectivity bottleneck, and network on chip (NoC) has evolved as an embedded switching network to address this issue. The interconnections between various cores or IP modules on a chip have a significant impact on communication and chip performance in terms of power, area latency and throughput. Also, designing a reliable fault tolerant NoC became a significant concern. In fault tolerant NoC it becomes critical to identify faulty node and dynamically reroute the packets keeping minimum latency. This study provides an insight into a domain of NoC, with intention of understanding fault tolerant approach based on the XY routing algorithm for 4×4 mesh architecture. The fault tolerant NoC design is synthesized on Field programmable gate array (FPGA).&lt;/p&gt;

Smart Emotion Detector by Using Python

The capability to perceive and comprehend human emotions holds significant importance across diverse realms of human interaction, ranging from enhancing human- computer interfaces to assisting in mental health diagnosis. This study presents an innovative Smart Emotion Detector, a resilient system engineered to precisely recognize and categorize facial human expression time. Face Detection entails the system's ability to identify human faces captured by the camera. In this context, we implement a digital attendance management system for classroom students utilizing facial recognition techniques.

Optimal and total controllability approach of non-instantaneous Hilfer fractional derivative with integral boundary condition.

The focus of this work is on the absolute controllability of Hilfer impulsive non-instantaneous neutral derivative (HINND) with integral boundary condition of any order. Total controllability refers to the system's ability to be controlled during the impulse time. Kuratowski measure and semigroup theory in Banach space yield the results. Furthermore, we talked about optimal controllability in conjunction with appropriate limitations. Our established outcomes are described using an example.

Conceptual design of a low-dose multi-parameter imaging system: Positron annihilation interaction-transmission imaging (PAITI)

The feasibility of Positron Annihilation Interaction-Transmission Imaging (PAITI), a novel 2D imaging modality based on positron annihilation photons, has been demonstrated through theoretical analysis and GATE Monte Carlo simulations. This system consists of two pixelated detectors on opposite sides of a point-like positron source. The object under investigation is positioned between the source and one of the detectors. By analyzing singles, coincidence, and anticoincidence counts, multiple 2D maps of the medium under investigation can be produced from a single data acquisition, such as the map of the number of occurred interactions, linear attenuation coefficient map, deposited energy map, and electron density map.Performance evaluations were conducted using simulated uniform phantoms (water, carbon, cortical bone, and titanium) and nonuniform phantoms (air and bone inside water). The average relative errors for determining the number of occurred interactions and attenuation coefficient were 4.42% and 5.0%, respectively. Additionally, the average relative errors for determining deposited energy and electron density were 3.15% and 4.7%, respectively. The system's capacity to discern fluctuations in electron density was also examined, demonstrating its capability to identify changes of 12.25% with a confidence level surpassing 95.3%, even at an absorbed dose much less than 15.14 ± 0.25 μGy.The results demonstrated the system's potential for future advancements and practical applications in medical imaging, specifically for low-dose multi-parameter anatomical imaging. In addition, the system's ability to determine electron density enables it to perform bone densitometry and ion range determination, with the latter being critical for patient-specific treatment planning for ion therapy.

AI monitoring and warning system for low visibility of freeways using variable weight combination model

AbstractIn intelligent vehicles, road environment perception technology is a key component of autonomous driving assistance systems. This component is the foundation for vehicle decision‐making and control, and is a guarantee of safety during the driving of the vehicle. The existing environment perception technology mainly targets well‐lit environments and requires visible light imaging equipment. Therefore, in low visibility environments, this technology cannot make good judgments about the external environment. Many existing perception systems mainly rely on sensors. Under low visibility conditions, these sensors weaken their effectiveness due to signal transmission, reflection, or absorption, resulting in incomplete or distorted data collection. Reduced visibility often affects the sensing range of various sensors, hindering the system's ability to detect and recognize distant objects, thereby limiting the necessary advance warning and response time for safe navigation. In response to this issue, this study proposed a combined method of infrared imaging and polarized imaging to collect feature data on road conditions in low visibility environments. Then, the obtained images were denoised and enhanced. The processed images were input into the system for recognition, and the images were analyzed and recognized using a low visibility road situation semantic segmentation algorithm based on deep learning. The research outcomes denoted that the pixel accuracy, average pixel accuracy, and average intersection ratio of the variable weight combination model in polarized degree images were 91.2%, 89.1%, and 71.6%, respectively. Those in infrared images were 83.6%, 90.6%, and 62.1%, respectively. The various indicators of the variable weight combination model were higher than those of the U‐shaped neural network model, indicating its performance is relatively excellent. The research results indicated that infrared imaging helps to acquire information at night or in low light conditions, while polarized imaging can provide better adaptation to cluttered light and reflections, enabling the system to provide more robust environmental sensing in complex weather conditions. It fills a critical gap in perception for autonomous driving systems in adverse weather conditions.