Objective: In the petrochemical industry, molecular reconstruction is crucial for understanding and optimizing the compositions of complex crude oil and petroleum products. As the first step of process simulation, quality control, and economic evaluation, precise molecular reconstruction approaches usually employ mathematical models to calculate the molecular compositions of petroleum products that align with their macroscopic properties. Traditional molecular reconstruction methods employ the gamma distribution to represent the carbon number distributions of homologs, but the coupling effects between the parameters "shape (α)" and "scale (β)" pose notable challenges in achieving desired interpretability and optimization efficiency. This study addresses these challenges by introducing a novel shape-decoupled parameter method that enhances the model's interpretability and simplifies the optimization process. Methods: The proposed shape-decoupled parameter method modifies a traditional gamma distribution by replacing the parameter's shape and scale with two new independent variables called peak position (m) and variance (σ2). Notably, m provides direct control over the zenith of the distribution, whereas σ2 independently determines the spread or width of the distribution, effectively reducing the coupling issue between parameters that exists in conventional gamma distribution models. Aiming at enhancing the stability and convergence speed during optimization, a multivariate linear regression (MLR) model was employed to estimate the initial parameter values. This regression model was trained on historical data of molecular compositions to provide reasonable initial values and decrease the probability of being trapped in local minima. The molecule-type homologous series (MTHS) matrix is used to represent the molecular composition of hydrocarbons, namely paraffins, isoparaffins, olefins, naphthenes, and aromatics (PIONA), with a comprehensive depiction of their multiple homologs. Moreover, an optimization problem was developed to minimize the prediction errors of the macroscopic properties, including molecular weight, density, PIONA group composition, and true boiling point curves. Upon a comparative analysis of multiple deterministic and heuristic optimization techniques, the differential evolution (DE) algorithm was determined as a favorable optimization tool by virtue of its superior accuracy and robustness. Results: Experimental evaluations showed that the shape-decoupled parameter method outperformed traditional methods in accuracy and optimization efficiency. Specifically, the density error decreased from 0.012 to 0.0059 g/cm3, and the average percentage relative error for the PIONA group composition also exhibits notable reductions. Moreover, the decoupled approach achieves faster convergence, requiring fewer iterations—reducing from 1 000 to as few as 20—without compromising accuracy. This reduction highlights the computational efficiency of the proposed method, which is a notable advantage in industrial applications with limited computational resources and time. Moreover, the proposed method exhibits enhanced robustness in addressing extreme molecular composition distributions, maintaining low errors in peak position and molecular composition predictions. This robustness becomes particularly evident when managing scenarios considered challenging by conventional methods, such as distributions with narrow ranges or hydrocarbons with approximately zero components at the boundary. Furthermore, the decoupled method provides better interpretability via independent control strategies for peak position and distribution width. The overall optimization performance was enhanced by the appropriate integration of the DE algorithm and effective initial parameter estimation by the MLR model. Conclusions: Compared with traditional methods, the proposed shape-decoupled parameter method provides a more interpretable, efficient, and accurate approach to the molecular reconstruction of petroleum products. By reducing the coupling effect between the parameters controlling the peak position and distribution width, this method simplifies the optimization process and achieves superior prediction accuracy and faster convergence. The results indicate the feasibility of its application for complex or extreme homolog distributions of hydrocarbons, revealing its higher reliability and robustness compared with traditional approaches. Future work is expected to focus on incorporating advanced machine learning techniques to further increase the accuracy and applicability of the model across a wider range of petroleum compositions, potentially enabling real-time molecular reconstruction for dynamic process optimization.
Objective: During chemical production, the different sampling frequencies of different variables generate a substantial amount of unlabeled data, which is challenging to use effectively, resulting in data waste. Additionally, distributed control systems frequently produce noisy data due to environmental interference and aging measurement instruments, complicating soft sensing modeling. Furthermore, in semi-supervised tasks, unsupervised components can undermine the accuracy of supervised tasks. To address these issues, this study proposes a semi-supervised soft sensing method for product quality based on a ladder network, enabling accurate, timely determination of key product quality and enhancing operational efficiency. methods: A two-step variable screening method—maximum mutual information (MIC) followed by minimum redundancy maximum relevance (mRMR)—was used to screen auxiliary variables. MIC was first applied to eliminate low-correlation variables, and mRMR was then used to remove redundant variables among the auxiliary set, yielding an optimal selection for modeling. The ladder network-based soft sensing method was then established, improving noise resistance by injecting disturbances into each encoder layer and reconstructing noise-free features layer by layer through the decoder. Skip connections were added between encoders and decoders to extract more information from unlabeled data, enhancing focus on supervised tasks and strengthening the model's robustness and generalization. Results: This method was applied to the methanol-to-olefin (MTO) process, termed DMTO. The MIC and mRMR screening reduced 203 auxiliary variables to an optimal 50. After preprocessing, several soft sensor models were established to compare outcomes. Results showed that unlabeled samples improved the effectiveness of supervised soft sensing tasks, with the proposed method enhancing various evaluation metrics. Residual analysis further indicated that the predicted residuals of the ladder network-based semi-supervised method closely aligned with a standard normal distribution, validating the method's superiority. Conclusions: Compared with supervised and other semi-supervised learning methods, the ladder network demonstrates superior prediction accuracy and generalization in soft sensing ethylene products in the DMTO process. The proposed approach offers promising applications for real-time monitoring and control of product quality in chemical production.
Objective: High-pressure gaseous hydrogen storage systems, such as large-scale hydrogen tank farms and distributed hydrogen refueling stations, are prone to hydrogen leakage, fire, and explosion because of the unique physicochemical properties of hydrogen. These events could set off a series of more serious accidents that would cause domino accidents. This study proposes a Bayesian network (BN)-based analysis method for the assessment of internal domino risk distribution within such systems. Methods: First, event tree models were established for various leakage scenarios in hydrogen-related facilities. Thereafter, all potential domino accident scenarios within the area were enumerated in calculation using accident consequence assessment models for hydrogen facility leaks. Next, BN models were automatically constructed to describe the propagation of domino accidents for each potential initial accident device. Finally, using BN models to analyze the magnitude and sources of overall risk for these systems, as well as the patterns of accident propagation and leakage scenarios. Results: The overall risk in hydrogen refueling stations mainly originates from the self-failure risk of compressors and the domino risk of hydrogen storage cylinders; jet fire (JF) and vapor cloud explosion (VCE) contribute 76% and 23.4% to the domino risk of all hydrogen cylinders, respectively. When the storage pressure in hydrogen tank farms is between 2 and 15 MPa, the domino risk comprises >25% of the overall risk, with explosions serving as the predominant accident type resulting in domino accidents. Causal reasoning indicates that a JF from a medium hole is the most probable domino accident scenario for both the hydrogen storage cylinders in the hydrogen refueling stations affected by the JF and the spherical tanks in the hydrogen tank farms affected by the explosion. Diagnostic reasoning for initial accident scenarios indicates that rupture and large-hole leakage of hydrogen spherical tanks and cylinders, respectively, are the most probable cause, provided that a multistage domino accident has occurred. Conclusions: Regarding the common 2-MPa hydrogen spherical tank employed in Chinese green hydrogen projects, the cumulative self-failure risk and domino risk of all tanks in the tank farms is 3.5×10-5 and 1.88×10-5 a-1, respectively, with the latter accounting for ~35%. In the future, decreasing the storage pressure to 1-1.7 MPa or increasing it to 10-15 MPa might lower the contribution of domino risk to < 30% and maintain cumulative self-failure risk at a level of 10-5 a-1. At 70-MPa hydrogen refueling stations, the domino risk to hydrogen cylinders from the compressors and pipeline is ~2.9×10-4 and ~4.4×10-5 a-1, respectively. In the abovementioned hydrogen storage systems, explosions are a notable accident type that can trigger domino accidents. Therefore, the implementation of explosion-suppression measures to decrease the probability of ignition is a key focus for mitigating the overall risk of hydrogen storage systems. Our findings indicate that future quantitative risk assessments for high-pressure hydrogen storage systems should consider the possibility of domino accidents. We believe these results serve as notable references for the establishment of advanced quantitative risk assessment methods customized to high-pressure hydrogen storage systems.
Objective: ID-based recommendation methods in recommender systems utilize unique identifiers of users or items to generate suggestions. However, these methods often encounter challenges such as data sparsity and cold-start problems, especially when using single-domain data. Cross-domain ID-based recommendations can help mitigate cold-start issues by relying on overlapping users or items across different domains. However, cross-domain ID information often lacks overlapped users or items. To address this, latent semantic patterns in behavioral networks across various recommender domains can be leveraged. This method aims to extract user preferences for items from discrete ID data, thereby tackling the limited shared information between these domains. Methods: Based on the study of interaction behaviors, this paper assumes the existence of latent pattern correlations between user-item interactions across different domains. A potential factor connects users across domains, leading some users to exhibit similar interaction behaviors in different contexts. These shared characteristics are referred to as interaction behavior semantic patterns. The proposed pattern-enhanced ID recommendation method enhances ID-based recommendations by leveraging these semantic patterns. In the target domain recommendation task, auxiliary domain information is introduced, and information from both auxiliary and target domains is jointly encoded using a graph neural network. By incorporating interaction behavior semantic patterns, user-item interaction and item description information from the auxiliary domain are transferred to the target domain. This process enhances the semantics of interaction behaviors in ID-based recommendations within the target domain. Results: This study conducts experiments on nine public datasets. User-item ID interaction data from datasets such as Yelp2018, Amazon-Kindle, Alibaba-iFashion, Amazon-Electronic, Book Crossing, MovieLens10M, MovieLens20M, and MovieLens25M serve as target domain datasets. Meanwhile, item description data from the Citeulike-a dataset is used as the auxiliary domain dataset. There are no overlapping user or item IDs between these domains. Experimental results show that the proposed method outperforms the current state-of-the-art methods, showing improvements in Recall@20 by 3%-30% and in NDCG@20 by 1% to 40%. Conclusions: This study proposes an ID recommendation method enhanced by interaction behavior semantic patterns based on the assumption of latent pattern correlations in user-item interactions across different domains. By introducing these semantic patterns, this method transfers user-item interaction information and item description information from the auxiliary domain to the target domain, thereby enhancing semantic understanding in ID-based recommendations within the target domain. Experimental results validate the ability of the proposed method to transfer semantic information in the absence of overlapping users and items across domains, yielding better recommendation performance. These findings validate the effectiveness of the proposed assumption and method. Additionally, experiments on ID recommendation tasks in multiple domains show that interaction behavior patterns between similar domains offer better transferability. The closer the auxiliary domain is to the target domain, the more notable the improvement in the target domain's ID recommendation results.
Objective: Social event detection involves the identification of trending events from various social event contents. It has garnered widespread attention in recent years. However, in practical scenarios, the performance of social event detection suffers in the case of data scarcity. Moreover, privacy concerns have resulted in regulatory restrictions preventing organizations from sharing data without explicit user consent, which makes centralized data training impractical. The current approaches primarily address the issue of data scarcity through cross-lingual knowledge transfer but often ignore the challenges associated with data privacy. Consequently, this study proposes a framework for cross-lingual social event detection based on federated knowledge distillation, referred to as FedEvent, with the goal of distilling knowledge from high-resource clients to low-resource ones. Methods: The framework employs parameter-efficient fine-tuning techniques and triple contrastive losses to effectively map non-English semantic spaces to English ones, and employs a federated distillation strategy to ensure data privacy. In addition, a four-stage lifecycle mechanism is designed to adapt to incremental scenarios. It includes four stages: pre-training of high-resource clients, pre-training of low-resource clients, client detection, and maintenance. In the first stage, a specific algorithm is used to train the initial model. In the second stage, the model trained in the first stage uses the central server as a medium for knowledge transfer to assist low-resource clients in training the initial model. In the third stage, the trained model is used to directly detect each incoming message. In the fourth stage, the model is continuously trained with the latest message blocks, and a federated codistillation mechanism is used to enable online learning for each client, allowing for the model to learn new knowledge. Based on the large-scale public English dataset Events2012, this study supplements event samples in Chinese and Vietnamese according to its topic descriptions, constructs private datasets for low-resource clients (Chinese-language client and Vietnamese-language client), and thereby establishes a multi-client, cross-lingual experimental environment. The performance of the framework is evaluated on the aforementioned datasets across two widely used clustering evaluation metrics: Normalized Mutual Information (NMI) and Adjusted Mutual Information (AMI). Results: 1) The experimental results demonstrate that compared with the existing methods, the proposed framework achieves effective knowledge transfer while ensuring data privacy. 2) In comparison with the single-node setup, the proposed framework demonstrates notable enhancement in the multimode environment on the Chinese-language client from 1.6% to 204.3% in NMI and from 2.0% to 342.9% in AMI. On the Vietnamese-language client, the respective improvements are noted to be between 2.3% to 6 200% and 0 to 4 400%. 3) The proposed framework performs very similarly to the state-of-the-art centralized method Cross-Lingual Knowledge Distillation (CLKD). 4) Case analysis reveals that FedEvent's outstanding performance on clusters with high-resource clients significantly influences its effectiveness on analogous clusters with low-resource clients. This demonstrates that FedEvent can effectively transfer knowledge from high-resource clients to low-resource ones. Furthermore, visual analysis vividly highlights the superior clustering outcomes achieved by FedEvent. Conclusions: The framework employs a lifecycle mechanism to accommodate the needs of event detection both in online and offline scenarios, effectively transferring knowledge through process and outcome supervision. Using knowledge distillation techniques, it mitigates the challenges faced when addressing low-resource languages and leverages a cross-lingual word embedding module to map semantic spaces between various languages. The proposed framework achieved the expected results, notably enhancing the model's performance.
Objective: Accurate weld seam recognition and automatic tracking control are crucial for ensuring the welding quality and operational efficiency of crawling robots. To achieve the efficient and automatic tracking of curved surface welds on large structural components, this work proposes a crawling robot bidirectional automatic tracking technology based on a single laser sensor and an adaptive weight welding gun cascade control method. Methods: A kinematic model was established for a crawling robot. The methods for estimating distance deviation between the laser system and the weld seam and correcting the angle between the robot and the weld seam were analyzed. By dynamically adjusting the position of the crawling robot with respect to the weld seam, the robot achieved bidirectional automatic tracking along the weld seam. Based on the welding process parameters and weld position information, the welding gun posture and end position were determined. The motion displacement value of the welding gun transmission joint was obtained by solving the inverse kinematics model of the actuator, and the joint motor was adjusted based on the motion displacement value for real-time welding gun calibration. Results: The influence of the distance between the laser system and the center of the robot on the straight weld path tracking was simulated and analyzed. Distances between 35 and 50 cm enabled rapid tracking of the weld seam by the laser system and center of the robot. The initial distance deviation had a small impact on the deviation between the laser system and the weld seam but has a significant impact on the angle correction between the robot and the weld seam. The stability conditions of the cascade control system were analyzed, and the bidirectional tracking performance of the robot along the weld seam was tested at the 5G and 6G welding positions. The distance deviation curve between the laser system and the weld seam during the tracking process and the angle correction curve between the robot and the weld seam were obtained. The distance deviation between the laser system and the weld seam was less than 2 cm, and the angle correction between the robot and the weld seam was approximately 1°. Conclusions: To ensure the stability of the cascade control system, the distance deviation between the laser system and the weld seam should be converted to the distance deviation of the robot tail for proportion integration differentiation (PID) input. The crawling robot motion control system satisfies the bidirectional automatic tracking along the weld seam in the 5G and 6G test scenarios, and the system has accurate welding gun positioning capability. Prealignment of the weld should be done before the welding operation of the crawling robot to further ensure operating stability.
Objective: Long-distance oil and gas transmission pipelines are important energy infrastructures. Currently, there are deficiencies in the automatic tracking accuracy and adaptability of external welding machines during pipeline construction. Operators often need to manually adjust external welding equipment (welding torch) to ensure the quality of the joints. Improving the intelligence of the welding process is an effective way to improve the efficiency and joint qualification rate during the on-site laying of long oil and gas pipelines. This study proposes a detection and control algorithm for the welding torch position and posture, applicable to all position welding of workpieces with arbitrary spatial postures. Methods: This study is the first to design a multisource sensor that combines laser-structured light vision sensing with dual-axis tilt sensing. This multisource sensor combines the advantages of both types of sensing, enabling it to detect the relative position information of the welding torch, as well as the posture information of the welding torch and workpiece. Using this multisource sensor, the algorithm performs integrated calculations of the welding groove size parameters and relative position and posture parameters of the welding torch under any workpiece posture through local groove surface reconstruction. This method fully uses laser line data from images to ensure stable, applicable, and accurate parameter calculations. Through coordinate transformation, the spatial posture (αw and βw) of the local workpiece can be obtained. These integrated feature parameters provide the basis for controlling the welding torch's spatial position and posture in any pipeline space all-position welding. Next, a pipeline intelligent welding system with five degrees of freedom based on multisource sensing is constructed. The system, combined with the designed algorithm, achieves real-time control of the welding torch position and posture (e, H, α, and β), meeting welding process requirements and enabling high-quality weld formation control during arc welding. Results: The experimental results show that the attitude angle feedback control error of the welding torch did not exceed 0.8°, the lateral position tracking deviation was within 0.25 mm, and the height tracking deviation did not exceed 0.63 mm during the pipeline all-position welding process. Compared to existing welding seam detection and tracking systems based on structured light-vision sensing, the proposed algorithm offers superior accuracy and stability. It detects not only the position deviation of the welding torch but also the posture of the welding joint on any unstructured surface with an unknown spatial posture. Conclusions: The proposed algorithm for detecting and controlling the position and posture of the welding torch can be used to achieve accurate control during pipeline space all-position welding. This advancement significantly improves the intelligence level of pipeline external welding equipment and provides technical support for controlling the position and posture control of the welding torch when welding unknown posture-curved workpieces.
Objective: Medical linear accelerator is a high-end piece of equipment used in cancer radiotherapy. A key component of this system is the multi-leaf collimator (MLC), which considerably influences the accuracy and efficiency of radiotherapy. This study examines the fast MLC, which employs a two-phase permanent magnet synchronous tubular linear motor (PMSTLM) to directly drive the leaves and enhance the precision of radiation dose distribution. However, factors such as inductance, magnetic chain, and resistance in the PMSTLM are influenced by temperature changes, resulting in parameter changes that affect the current loop's steady-state error. Moreover, drastic fluctuations in current result in increased energy consumption. The heightened energy usage negatively impacts the blade motion accuracy, thus compromising the overall quality of radiotherapy. The objective of this study is to address these challenges by reducing the energy consumption of linear motors used in MLC. Methods: This study proposes a method for optimizing the energy consumption of linear motors in MLC by improving velocity planning at the instruction level of the control system. To address the issue of kinematic constraints not fully utilizing the motor thrust, a dynamic friction coefficient is introduced to determine the moment boundary of the motor thrust. Based on this boundary, an improved exponential acceleration and deceleration speed planning method is developed. Furthermore, acceleration distance and deceleration characteristic coefficients are introduced as independent variables. The mapping relationship between full-stroke energy consumption, the transition time of the displacement section in the middle of the motor travel, and these coefficients is established. Using this relationship, an optimization model for energy consumption, transition time, and speed planning is formulated. The second-generation non-inferiority sorting genetic algorithm (NSGA-Ⅱ) is employed to perform multi-objective optimization of energy consumption and transition time. The result is utilized as commands for the controller and is validated through experimental testing. Results: Through the proposed method, this study achieved acceleration and deceleration planning results with constant transition time and relatively low energy consumption for the full stroke. Experimental data indicate that the method reduces energy consumption by 21.5%, compared to trapezoidal acceleration-deceleration planning (TSP) under identical transition time conditions. The proposed method effectively reduces the energy consumption of PMSTLM operation while maintaining the normal functional requirements of the MLC. Conclusions: The energy consumption optimization method proposed in this paper combines exponential acceleration and deceleration planning, an energy consumption-transition time speed planning model, and the NSGA-Ⅱ algorithm to enhance the performance of MLC. Based on theoretical research and experimental validation, the following conclusions are drawn. The proposed method can optimize the intermediate displacement section corresponding to the transition time by effectively utilizing the velocity peak. This is achieved by choosing appropriate acceleration distances and deceleration characteristic coefficients. Transition time and energy consumption are conflicting optimization objectives. By employing the energy consumption-transition time optimization model, choosing appropriate optimization parameters can considerably reduce energy consumption while ensuring that the transition time meets the performance requirements of the MLC. The experimental results verify the effectiveness of the proposed method. Energy consumption is reduced by 21.5% compared to that of the TSP method.
Objective: Predicting the remaining useful life (RUL) of industrial equipment is critical for maintaining safe operations and minimizing maintenance costs. However, RUL prediction for edge devices faces several challenges. First, edge devices often lack the computational power and storage capacity required for complex RUL prediction algorithms, making such predictions difficult. Many RUL prediction algorithms require substantial resources, which are scarce on edge devices. Second, the limited data transmission rate between the cloud and edge devices causes high latency when transmitting large data sets to the cloud, affecting real-time predictions and increasing network bandwidth usage. Additionally, data sharing among all edge devices is often impractical owing to privacy, security issues, and potential conflicts of interest, limiting models to local data and reducing their accuracy. Methods: To address these challenges, this paper proposes a cloud-edge collaboration framework for RUL prediction based on federated learning. The framework comprises two main processes. In the first process, each training device trains a variational autoencoder (VAE) using its local data set. The trained encoders are then uploaded to the cloud and aggregated using a weighted average method (FedAVG), with the number of training samples as weights. The aggregated global encoder is then downloaded to all edge devices. In the second process, the aggregated encoder extracts hidden features from the local data sets on each edge device. These features are uploaded sequentially to the cloud to train the RUL predictor. Once trained, the predictor is sent back to the edge devices, completing one training cycle. This iterative process continues until a well-trained RUL prediction model, consisting of the global encoder and predictor, is achieved. During the testing stage, the global encoder is used to extract hidden features, while the RUL predictor performs deeper feature extraction and RUL prediction. In this framework, only local encoders and hidden features are uploaded to the server, significantly reducing communication overhead. Most of the training occurs on the server, with clients only performing the basic training of the shallow VAE, thereby effectively utilizing the server's powerful computational capabilities. Data privacy is maintained since the server receives hidden features and encoders, not the original data, preventing data reconstruction. Results: To validate the proposed method's efficiency and practicality, different network structures were tested for RUL prediction on the commercial modular aero-propulsion system simulation (C-MAPSS). Although there was a slight decline in prediction performance compared to the baseline, the difference was within acceptable limits. This minor trade-off in accuracy enabled RUL prediction under resource constraints. The proposed algorithm significantly reduced data transmission time after feature extraction across various data scales consistently. In industrial scenarios with large data volumes, this reduction was even more pronounced. Further validation using nuclear power unit fault data sets showed a slight decrease in root mean square error (RMSE) on the test set without a significant drop in prediction accuracy. These results demonstrate that the proposed cloud-edge collaboration framework is promising for fault diagnosis in nuclear power units, effectively addressing edge resource limitations. Conclusions: The proposed cloud-edge collaboration framework leverages federated learning to achieve RUL prediction on resource-constrained edge devices, thereby alleviating issues related to resource constraints and data privacy. By employing VAE-based feature extraction and federated learning for model training, the framework achieves efficient model training while significantly reducing communication overhead with minimal impact on accuracy. Experimental validation on industrial simulation data sets and nuclear power unit fault data sets demonstrates the framework's practicality and effectiveness. This framework represents a useful approach to addressing challenges in fault diagnosis and URL prediction within resource-constrained settings.
Objective: The square-shaped dual-chamber floating oscillating water column (OWC) wave energy converter is designed to convert wave energy through the heave motion of its floating structure. This device uses airflow channeled from the dual chambers to drive a turbine generator, making it essential to investigate its primary energy conversion characteristics. Methods: Numerical calculations and experimental tests were conducted to study the model's capture performance. Hydrodynamic software was used to simulate the response of the dual-chamber floating OWC wave energy model under different wave conditions. Regular wave experiments verified the accuracy of these simulations and evaluated the model's performance, while irregular wave experiments assessed its capture performance in real marine environments. Results: The numerical analysis indicated that the motion response of the dual-chamber wave energy model peaks near the heave natural period of the floating body, optimizing energy capture. It was also found that the angle between the incident wave direction and the model significantly affects performance. When the chambers are aligned front to back (0° angle), energy capture is maximized, suggesting this arrangement is the most effective. To verify the numerical calculations and assess the actual performance of the wave energy model, regular wave experiments were carried out. These experiments demonstrated that when the dual chambers of the floating OWC wave energy model are arranged front to back, the capture performance is superior compared to the left-right arrangement. The optimal capture performance periods for the front and back chambers of the model are not aligned, allowing the front-to-back chamber arrangement to broaden the range of optimal response periods, thereby enhancing the system's overall energy capture efficiency. Additionally, to evaluate the capture performance of the dual-chamber floating OWC wave energy model in real marine environments, irregular wave experiments were conducted. The experimental results showed a maximum capture width ratio of 41.84% under irregular wave conditions, which is close to 84% of its performance under regular waves. This indicates that the dual-chamber wave energy model maintains strong energy capture capability and stability even in challenging marine conditions. Conclusions: Combining the results of numerical calculations and experimental tests, the dual-chamber floating OWC wave energy model exhibits excellent energy conversion performance across different wave conditions. The innovative front-to-back arrangement design of the dual chambers significantly enhances capture performance and broadens the range of optimal response periods. This research provides new ideas and methods for the development of wave energy conversion technology. The results have significant implications for optimizing and practically applying wave energy solutions, and they are expected to promote the development and utilization of marine renewable energy, thereby contributing positively to the advancement of green energy.
Objective: The distributing pipe in a Pelton turbine serves as a crucial water supply component responsible for regulating flow and inducing diversion. Its special structure, however, can lead to adverse effects such as flow separation and Dean vortices causing hydraulic losses; these losses can vary with changes in the upstream head, further affecting the incoming flow conditions. Traditionally, the pressure drop method has been primarily utilized to assess these losses, yet it fails to pinpoint the exact locations where significant hydraulic losses occur. Methods: This study investigates the hydraulic and loss characteristics of the distributing pipe. Utilizing the SST(shear stress transport) k-ω turbulence model, we simulate the flow inside the distributing pipe and analyze entropy production distribution based on the entropy production theory. Then, according to the distribution of entropy production rate and flow pattern, the reasons for the hydraulic loss in the main channel and bifurcation 2 were analyzed detailly. Entropy production—indicative of irreversible dissipative effects during fluid flow—effectively highlights high hydraulic loss areas by converting lost mechanical energy into internal energy. Results: Results show a remarkable increase in total entropy production within the pipe, with values rising from 210.999 to 4 614.980. Specifically, entropy production in the main channel increases from 145.549 to 3 477.351, and in bifurcation 2 from 38.857 to 717.608. Under high-speed flow conditions, the separation between internal and external flows becomes distinct, particularly when fluid navigates bends. The hydraulic loss is dominated by fluctuation entropy production, accounting for >50%. The main flow zone and bifurcation 2 are the primary sites of hydraulic loss, accounting for approximately 90% of the total loss, whereas bifurcations 1 and 3 experience relatively small losses. Conclusions: Comparative analysis of entropy generation rate contours, streamline plots, and pressure fluctuation curves highlights that high entropy generation areas experience significant pressure pulsations, accompanied by adverse flow phenomena such as Dean vortices and flow separation. At bifurcation 2, high-speed fluid is diverted and squeezed outward, creating a low-pressure vortex on the inner side, inducing significant hydraulic loss. At the bend position, the fluid tends to flow outward, resulting in high external pressure and low internal pressure distribution at the ring pipe and further in high hydraulic loss on the inside. These phenomena create large pressure gradients and significant pressure fluctuations, affecting flow stability. Furthermore, optimization strategies are proposed for the distributing pipe design, including the addition of flow-diversion baffles at bifurcation points to stabilize flow patterns, reduce vortices, and alleviate flow separation by increasing the number of nozzles and reducing curvature. This study employs numerical computation to investigate the mechanisms of hydraulic loss generation within the distributing pipe and meticulously delineates areas of high hydraulic losses, offering hydro turbine developers optimization strategies.
Objective: In-wheel motor drive systems offer significant advantages for electric vehicles, including large chassis space, high transmission efficiency, and great control flexibility. However, in current mainstream in-wheel motor driving vehicles, the unsprung mass is significantly increased because the motor or the driving unit is rigidly connected to the wheel hub. The increased unsprung mass not only deteriorates vehicle ride comfort and road holding performance, but also results in heavy motor vibration. To mitigate these negative effects, configurations with suspended motor or driving unit have been proposed. It is thus desirable to explore the potential of these new configurations in this regard. Methods: This paper aims to mitigate the negative effects of unsprung mass by optimizing vehicle and motor suspension parameters simultaneously. To this end, it examines two typical in-wheel motor drive configurations with motor suspension: the dynamic vibration absorber configuration and the two-stage suspension configuration. Half-vehicle models are established respectively for both configurations, and key indices for vehicle dynamic performance are selected or defined. Drawing on earlier studies on how the increased unsprung mass impacts vehicle performance at various speeds, and considering the trade-off among ride comfort, road holding, and motor vibration, a multiobjective optimization strategy is proposed for parameter optimization of vehicle suspension and motor suspension. In the strategy, the goal is to minimize body vertical acceleration, wheel dynamic load, and motor acceleration at medium speeds while reducing body pitch acceleration, wheel dynamic load, and motor acceleration at high speeds. Constraints include the natural frequency and dynamic deflection of the vehicle suspension. Using the NSGA-Ⅱ algorithm, Pareto optimal solution sets are derived respectively for the two configurations. The entropy weight method is then applied to determine the optimal parameters for vehicle and motor suspensions. With the optimal suspension parameters, dynamic simulations are conducted on a random road, and the dynamic performance is evaluated based on the predefined indices. Results: The results indicate that, compared to the fixed hub motor configuration, both motor suspension configurations achieve a substantial performance enhancement in vehicle ride comfort, road holding, and motor vibration. Specifically, the dynamic vibration absorber configuration delivers greater enhancements in vehicle body vertical and pitch vibrations, as well as wheel dynamic load. Specifically, it reduces body vertical and pitch accelerations by 36.9% and 33.09%, respectively, at medium and high speeds. The wheel dynamic load is decreased by 18.42% and 18.55% at medium and high speeds, respectively. By contrast, the two-stage suspension configuration excels in reducing motor vertical vibration. It reduces motor vertical acceleration by 67.48% and 65.43% at medium and high speeds, respectively. Conclusions: This paper presents a passive control approach to address the negative effects of unsprung mass by utilizing motor suspension configurations. The in-wheel motor drive configurations with motor suspension demonstrate significant potential for improving vehicle dynamic performance. This research serves as a valuable resource for the design of in-wheel motor driving vehicles.
Objective: Precise predictive control of braking torque is the foundation for autonomous driving of heavy-duty trucks. A hydraulic retarder is an auxiliary braking device widely used in these vehicles. Regulating the filling rate in its working chamber is the key to predicting and controlling the output braking torque of a hydraulic retarder. However, the working chamber is an open chamber system. The dynamic change in its filling rate is challenging to measure and characterize, which greatly affects the accurate prediction and control of the braking torque. Methods: First, the impact of the change in the filling rate on gas pressure in the hydraulic retarder's oil storage chamber was analyzed. An increase or decrease in the filling rate corresponds to an increase or decrease in gas volume in the oil storage chamber. The gas pressure will fluctuate as the filling rate changes. Thus, measuring the dynamic variation of oil volume in the working chamber, as used in traditional methods of measuring the filling rate, was converted into monitoring gas pressure fluctuations in the oil storage chamber using pressure sensors. Subsequently, a feedforward proportional integral control algorithm with allowable deviation for the control pressure of the hydraulic retarder was designed. A new method for measuring the filling rate was established based on the target control pressure, actual control pressure, allowable deviation of the control pressure, and critical volume design parameters of the hydraulic retarder without additional flow sensors. To verify the effectiveness of the established method for measuring the filling rate, bench tests were conducted on a hydraulic retarder prototype. During the bench test, according to the four fixed gears of hydraulic retarders, four target gas pressures were set to 60, 130, 220, and 280 kPa, which allowed a deviation of 10 kPa. Results: The measured filling rate under the four control pressures showed that at constant gas pressure, the filling rate decreased with increasing rotational speed. When the rotational speed was the same, higher gas pressure corresponded to a higher filling rate, consistent with the actual situation. Because the differences in each exhaust process were neglected, the established measurement method calculated a discontinuous filling rate during the exhaust process. However, this did not affect the overall trend in which the filling rate decreased with increasing speed. The filling rate obtained by the method in this paper was compared with the theoretical calculation method in the literature, which showed that under four different control pressures, the filling rate results obtained by the two methods exhibited the same trend and were close, indirectly confirming the effectiveness of the proposed method. Conclusions: By monitoring the gas pressure fluctuations in the oil storage chamber using a pressure sensor, a method for measuring the filling rate of the hydraulic retarder was established. This method was based on the target control pressure, actual control pressure, allowable deviation of the control pressure, and critical volume design parameters of the hydraulic retarder, eliminating the need for additional flow sensors. The effectiveness of the established filling rate measurement method was verified through bench tests and mutual verification with the calculation results of methods in the literature. The established method for measuring the filling rate can provide critical core parameter support for accurate prediction and control of the braking torque of hydraulic retarders as well as research and development design optimization.
Objective: In the context of the swift progression of autonomous driving technology, the widespread reliance of current systems on uniform behavioral models for decision-making and path planning is a crucial concern. This generalized approach often disregards variations in driving behavior among different drivers, making it challenging to achieve driving behavior that aligns with drivers' expectations in complex and dynamic traffic scenarios. Consequently, a decrease in comfort and trust is observed in autonomous vehicles. This study focuses on lane changing, a common yet critical driving maneuver, aiming to optimize planning strategies by incorporating drivers' characteristics to match individual driving styles. Methods: This study comprehensively analyzes data derived from naturalistic driving experiments. Kalman filtering is used to detect and eliminate anomalies in raw data, thereby reducing noise interference. The integration of temporal constraints into the fuzzy C-means clustering algorithm ensures the preservation of chronological order in the clustered data, which is essential for analyzing sequential events such as lane change maneuvers. Lane changing requires lateral and longitudinal vehicle control with distinct operational characteristics across different phases of the maneuver. By clustering the entire lane-changing process data into three major categories, C1, C2, and C3, representing the preparation, execution, and completion stages of lane changing, respectively, this study aims to analyze disparities in driver behavior during these distinct phases. According to the characteristics of lane-changing scenarios, relevant variables are selected for in-depth examination. Independent sample t-tests are then conducted among different drivers for each variable, and variables with a high proportion of insignificant t-values are eliminated. This process helps identify personalized indicators that reflect driver-specific traits during lane changing. Subsequently, an artificial potential field (APF) model is established for the lane-changing scenario. The APF method uses virtual attractive and repulsive forces to guide the vehicle toward a path of decreasing potential energy, effectively avoiding obstacles while moving toward the target position. Variations in the APF parameters lead to different planning paths. By leveraging the extracted personalized indicator, the APF model for lane changing is customized, yielding paths that align with individual driving styles. Another pivotal consideration is the planning of lane-changing speeds. Given the notable variations in the speed preferences of drivers, this study proposes a lane-changing speed planning algorithm based on a quintic polynomial function. This ensures that the mean duration of acceleration and the maximum acceleration limit during the execution phase align with each driver's speed control habits and that a smooth velocity profile is maintained throughout the lane-changing maneuver. Conclusions: This study proposes a lane-changing planning method for autonomous vehicles that considers driver differences. The simulation results confirm that the proposed personalized lane-changing planning approach not only produces paths that align with individual driving styles but also regulates lane-changing velocities in accordance with each driver's operational habits. By quantifying behavioral variations, developing personalized APF models, and implementing customized speed planning strategies, this study exemplifies how to tackle individualization challenges in autonomous driving. This study represents a step forward in advancing autonomous vehicle technology toward a human-centric and intelligent future.
Objective: Mobility as a service (MaaS) plays a critical role in urban transportation networks, leveraging communication technologies to provide integrated travel information while promoting sustainable development. Despite its importance, the application of MaaS in tourism traffic still needs to be explored. Methods: This study focused on tourists visiting Hangzhou and designed a survey questionnaire for data collection. A combined approach of stated preference and revealed preference methods was employed to examine travel behavior within the context of tourism traffic. Using data from an online platform, the study analyzed tourists' travel mode choices and their preferences for different MaaS solutions. First, a nested Logit model was developed, with the upper level representing multimodal transportation choices while the lower level analyzed their MaaS solution selections. Influencing factors were categorized into three main groups: individual characteristics, household attributes, and travel-related factors. Given the potential nonlinear relationships between these influencing factors and MaaS solution choices, a light gradient boosting machine model was further applied. This machine learning model explored interactive effects between various factors on MaaS choice, such as how age and income jointly influence MaaS preferences or how the choice of tourist attractions relates to tourist demographics. To further elucidate the collaborative effects of key factors, the Shapley additive explanations method was incorporated to interpret the collaborative effects of multiple factors on MaaS decisions. Results: The results indicate a strong connection between MaaS solution choices and tourists' preferred travel modes, validating the use of the nested logit model. Several key factors emerged as significant influencers of MaaS preferences, including income, occupation, household lifecycle, daily travel habits, and the type of tourist attractions visited. Specifically, self-employed individuals, those who frequently use public transport, and tourists who plan fewer bus trips showed a preference for MaaS solutions combining discounts on subways and taxis. Groups traveling with elderly individuals or those accustomed to "public transport and private car" habits leaned toward MaaS solutions that offer unlimited subway rides. Additionally, the type of tourist attractions plays a crucial role in shaping MaaS preferences. Tourists visiting commercial, entertainment, or cultural heritage sites, particularly from lower-income groups, chose MaaS solutions with unlimited bus rides. Meanwhile, those visiting historical landmarks favored taxi-centered MaaS options. Tourists aged 45 and above were less likely to select MaaS solutions with unlimited subway rides as their income rose. Older tourists visiting historical sites showed a stronger preference for MaaS solutions, prioritizing taxi services. Public transport users who gravitate toward subway-based MaaS solutions share common traits, such as being in low-income groups, self-employed, traveling with elderly companions, and regularly transferring between public transport modes in their daily routines. Conclusions: These findings can guide public transportation agencies, tourism operators, and other stakeholders in designing user-centered MaaS solutions. By segmenting users based on demographics and travel habits, personalized travel services can be created to improve the applicability of MaaS in tourism transportation. The conclusions of this study have significant implications for promoting the use of public transportation, improving the operational efficiency of multimodal urban transportation networks, and increasing the attractiveness of transportation options for tourists.
Objective: Previous studies on the optimal mooring system of a submerged floating tunnel (SFT) have mostly focused on numerical methods, with a limited number of experimental studies conducted under regular waves. The primary evaluation factors used for identifying the advantages and disadvantages of different mooring systems have been the maximum motion response and mooring tension in the time domain. To evaluate the optimal mooring system of SFT more effectively and comprehensively, this study introduces several new evaluation parameters alongside the maximum motion response and mooring tension. Methods: Physical modeling tests were conducted under regular and irregular waves, focusing on the effect of different mooring systems on the hydrodynamic characteristics of the SFT. Based on the time history of motion responses and mooring tensions obtained from the tests, the advantages and disadvantages of different mooring systems were comprehensively examined based on three evaluation factors: the maximum motion response and its spectral area, the maximum mooring tension and its distribution uniformity, and the frequency of cables reaching a relaxed condition during wave actions. Results: Results revealed the following: (1) The diagonal-cable systems CM3 (four diagonal cables at either end of the SFT) showed better performance than those of the "diagonal cable + vertical cable" system CM1 (two diagonal cables and one vertical cable at either end of the SFT) and CM2 (two diagonal cables and two vertical cables at either end of the SFT). (2) The natural periods of three mooring systems were observed to follow the order of CM1 > CM2 > CM3. In the mooring system CM3, the natural periods of surge and heave were only approximately 50% of the mooring system CM1. Meanwhile, the natural period of pitch was significantly reduced to approximately 25% of the mooring system CM1. In addition, the natural period of pitch was significantly shorter than those of surge and heave, indicating that the mooring system CM3 had a strong constraint effect on pitch. (3) The 0th-order moments (spectral areas) of the dynamic response reflect the overall degree of the motion response. The 0th-order moments of the motion response and mooring tension under three mooring systems followed the order of CM1 > CM2 > CM3. In the mooring system CM3, the spectral areas of surge, heave, and pitch of the SFT were significantly lower than those in the other two mooring systems, and the difference was particularly significant under large waves. (4) The uniformity of mooring tensions is crucial for determining an optimal mooring system. For the three mooring systems, the ratios of the maximum mooring tensions of all cables at either end of the SFT were as follows: CM2 1.0-1.36, CM2 1.0-1.37, and CM3 1.0-1.12. The distribution uniformity of the mooring tensions of all cables in the mooring system CM3 was better than those in the other two mooring systems. (5) The cables in the mooring system CM2 attained a relaxed or nearly relaxed state most frequently, while the mooring system CM3 exhibited the lowest number of such instances. Conclusions: Compared to the maximum motion response and mooring tension, analyzing the maximum motion response and its spectral area, the maximum mooring tension and its distribution uniformity, and the frequency of cables reaching a relaxed condition provides a more effective and comprehensive method for evaluating the advantages and disadvantages of the mooring system of SFT. These evaluation parameters and methods are crucial for guiding project design.
Objective: The dimensional expansion of bronze-processed Nb3Sn wires during heat treatment presents difficulties in the design and fabrication of high-field superconducting magnets, especially for ultrahigh-field magnetic resonance imaging (MRI) magnets at 14 T and above. Thus, the characteristics of the dimensional changes in the bronze-processed Nb3Sn wires during heat treatment must be determined. This study considered bronze-processed Nb3Sn wires reinforced with NbTi-CuNi in a 14 T animal MRI magnet as an example to analyze its dimensional change during heat treatment. The dimensional change rates of the Nb3Sn wire were used as a basis to discuss the influence of the dimensional expansion of the Nb3Sn coil during heat treatment on the electromagnetic properties of the 14 T animal MRI magnet. Methods: The volume ratio of each component in the Nb3Sn wire was analyzed before and after heat treatment. Based on the material properties of each component of the Nb3Sn wire, the rate of change in length during the heating and cooling stages of heat treatment was calculated using an established finite element model. The sum represents the rate of length change during heat treatment. Based on the phase transformation mechanism of Nb3Sn wires during heat treatment, the ratio of voids in the Nb3Sn wire during heat treatment was calculated, and that of each component of the wire was added to determine the range of changes in the cross-sectional area of the Nb3Sn wire during heat treatment. An originally designed experimental apparatus was built to measure the change rate of the circumference of the Nb3Sn single-layer solenoid coil during heat treatment. The measurement results for the single-layer solenoid coil and straight wire were compared. In addition, the measurement findings were compared with the values obtained through calculation. Based on the average dimensional change rates of Nb3Sn wires, we calculated the dimensional expansion of the Nb3Sn coil in a 14 T animal MRI magnet during heat treatment and determined the magnetic field homogeneity and radial Lorentz force along the eccentric direction after heat treatment. Results: The calculation results indicate that the wire had a length change rate of 0.5% during heat treatment and a cross-sectional area change ranging between 0%-4.7%. According to the measurement results, the length change rates of the single-layer coil and straight wire during heat treatment were 0.55% and 0.52%, respectively, whereas the cross-sectional area change rates of the wires were 1.98% and 2.22%, respectively. The expansion of the inner diameter, outer diameter, and axial length of the Nb3Sn coil in the 14 T animal MRI magnet during heat treatment reached 1.19, 2.04, and 7.44 mm, respectively. The magnetic field homogeneity of the magnet changed from 1.1×10-6@6 cm DSV to 31×10-6@6 cm DSV and 45×10-6@6 cm DSV in the cases of zero radial eccentricity and a radial eccentricity of 0.595 mm in the Nb3Sn coil. In addition, the radial eccentricity of the Nb3Sn coil will produce a 1.9×104 N Lorentz force along the eccentricity direction. Conclusions: As the dimensional change rates of the single-layer coil and straight wire during heat treatment were the same, the effect of coil winding on the dimensional change of the Nb3Sn wires during heat treatment was negligible. Moreover, the measured change rate of the wire length was similar to the calculation result, and the measurement finding of the change rate of the wire cross-sectional area fell within the calculation range.
Objective: Natural ventilation in real residential environments is characterized by dynamic multizone airflow. Ignoring either of the two features can lead to inaccurate assessments of natural ventilation in residences. The multiple tracer gas method has traditionally been used to investigate this multizone airflow within residential settings. However, it is impractical for large-scale applications owing to the high costs associated with the measurement process, potential disruption to occupants' daily lives, and the need for stable ventilation conditions. As a result, accurately measuring air exchange rates remains a significant challenge. A more in-depth study of the measurement method for dynamic multizone airflows is urgently required. This study proposes a simulation method for identifying dynamic multizone airflows in naturally ventilated residences. Methods: This method utilizes CO2 emitted by occupants as a tracer gas to study multizone airflow in residential buildings. It considers all feasible multizone airflow patterns using a traversal approach and the air volume conservation principle. Furthermore, to strike an optimal balance between effectively tracking the dynamic characteristics of natural ventilation and minimizing noise sensitivity, a transient indoor CO2 mass balance equation associated with the Kalman filter is applied to each zone. The resulting time series of air exchange rates can be presented for each airflow pattern. These rates are then evaluated to identify the pattern that most closely aligns with the actual airflow pattern and the corresponding outdoor-indoor air exchange rates and interzonal airflow rates. Furthermore, two validation experiments were conducted in an unoccupied two-bedroom apartment with controllable ventilation patterns to validate the method. Subsequently, the method was employed in an occupied apartment, utilizing measured indoor CO2 concentrations and occupancy data for each zone to produce the time series of air exchange rates. Results: The comparison among the calculated air exchange rates using the proposed method and experimental data indicates that 85% of the calculated values have absolute errors within RHHZ_177;0.2 h-1, and 95% fall within RHHZ_177;0.4 h-1. Furthermore, 75% of the calculated values have relative errors within RHHZ_177;10%, and 95% are within RHHZ_177;20%. The calculated air exchange rates and airflow directions closely match the experimental conditions, indicating that the method proposed in this study effectively represents the multizone aspects of natural ventilation in residential environments. Moreover, the applicability of the method to real residences is demonstrated through its application in an occupied apartment. The calculated air exchange rates for each zone during the measurement period, after filtering out anomalous results, fall within a reasonable range. These results present the airflow patterns that characterize the multizone nature of dynamic natural ventilation. Conclusions: Natural ventilation is complex owing to its multizone nature and time dependence, leading to data scarcity. This method effectively addresses this gap by quantifying the multizone representation of dynamic airflows in residences on a large scale. Understanding indoor–outdoor air exchange rates and interzonal airflow rates is pivotal, as these parameters significantly influence indoor thermal conditions and air quality. In this regard, this study offers a valuable and practical approach to comprehensively understanding natural ventilation and its effects on occupants' health conditions in real residential environments.
Objective: Nuclear medicine imaging is a dynamic imaging technique that enables researchers to analyze the physiological and pathological processes, especially in the brain. When imaging awake and unconstrained mice, the free movement of their heads can cause motion artifacts in the nuclear medicine images. These artifacts reduce image resolution, decrease the concentration of the tracer in the region of interest, and affect the quantification of the standard uptake value and the estimation of tracer kinetic model parameters. Therefore, the elimination of head motion artifacts is crucial for improving the quality of brain positron emission tomography (PET) images. In recent years, some researchers have been using markers attached to the mice's heads to track their movement. However, attaching markers to the mice's heads may cause discomfort and anxiety. In addition, the freedom of movement of the head during imaging can lead to relative sliding or detachment of the markers, resulting in incorrect motion estimation. Methods: In this study, we design a mouse head motion tracking system based on the you only live once (YOLO) v5 algorithm. This system can accurately monitor the position and posture of the mice's heads in real time, providing precise motion information for motion correction in nuclear medicine images. In contrast to traditional motion tracking systems, this system does not require markers attached to the mice's heads, effectively addressing the limitations of previous tracking methods. The proposed motion tracking system consists of three main stages, namely feature point recognition and positioning, three-dimensional reconstruction of feature points, and calculation of the rotation and translation parameters. First, the YOLO v5 algorithm automatically identifies and locates existing feature points on the mice's heads to obtain the pixel coordinates of each feature point. Then, using the parallax effect and triangulation principles, we reconstruct the three-dimensional coordinates of the feature points in the world coordinate system. Finally, we calculate the Euler angles of the mice's heads using the symmetry of the feature points and utilize inter-frame pose differential methods to compute the translation and rotation parameters of the head pose change between adjacent frames. Results: To verify the performance of the designed motion tracking system, we place a mouse phantom in a stationary position and measure the changes in its head position and posture angles using the designed system. The experimental results show that for the X, Y, and Z axes, the root-mean-square errors of the translational degrees of freedom are 0.04, 0.19, and 0.03 mm, whereas the root-mean-square errors of the rotational degrees of freedom are 0.58°, 0.34°, and 2.03°. We use the MATLAB function to obtain the histogram statistics of the detected translation and rotation parameters, all of which conform to a normal distribution. Conclusions: The results indicate that the designed motion tracking system can accurately monitor the movement of the mice's heads during nuclear medicine imaging. The detected parameters of six degrees of freedom conform to a normal distribution, further confirming the reliability of the system. Moreover, this system does not rely on markers, effectively avoiding the risk of marker detachment. The motion data obtained through this system can be used to compensate for and correct motion artifacts of the mice's heads in nuclear medicine imaging, thereby enhancing the quality of nuclear medicine images.
