A real-time tuna detection network on mobile devices is a common tool for accurate tuna catch statistics. However, most object detection models have multiple parameters, and normal mobile devices have difficulties in satisfying real-time detection. Based on YOLOv3, this paper proposes a Tuna-YOLO, which is a lightweight object detection network for mobile devices. Firstly, following a comparison of the performance of various lightweight backbone networks, the MobileNet v3 was used as a backbone structure to reduce the number of parameters and calculations. Secondly, the SENET module was replaced with a CBAM attention module to further improve the feature extraction ability of tuna. Then, the knowledge distillation was used to make the Tuna-YOLO detect more accurate. We created a small dataset by deframing electronic surveillance video of fishing boats and labeled the data. After data annotation on the dataset, the K-means algorithm was used to get nine better anchor boxes on the basis of label information, which was used to improve the detection precision. In addition, we compared the detection performance of the Tuna-YOLO and three versions of YOLO v5-6.1 s/m/l after image enhancement. The results show that the Tuna-YOLO reduces the parameters of YOLOv3 from 234.74 MB to 88.45 MB, increases detection precision from 93.33% to 95.83%, and increases the calculation speed from 10.12 fps to 15.23 fps. The performance of the Tuna-YOLO is better than three versions of YOLO v5-6.1 s/m/l. Tuna-YOLO provides a basis for subsequent deployment of algorithms to mobile devices and real-time catch statistics.

Aiming at the problems of large statistical error and the poor real-time performance of catch weight in the ocean fishing tuna industry, an algorithm based on improved YOLOv8-Pose for albacore tuna (Thunnus alalunga) fork length extraction and weight estimation is proposed, with reference to the human body’s pose estimation algorithm. Firstly, a lightweight module constructed using a heavy parameterization technique is used to replace the backbone network, and secondly, a weighted bidirectional feature pyramid network BIFPN is utilized. Finally, the upper and lower jaw and tail feature points of the albacore tuna (Thunnus alalunga) were extracted using the key point detection algorithm, and the weight of the albacore tuna (Thunnus alalunga) was estimated based on the fitted relationship between fork length and weight. The experimental results show that the improved YOLOv8-Pose algorithm reduces the number of model parameters by 13.63% and the number of floating-point operations by 14.03% compared with the baseline model without decreasing the accuracy of the target detection and key point detection and improves the model inference speed by 374%. At the same time, it reduces the drift of the key point detection, and the error of the comparison with the actual albacore tuna (Thunnus alalunga) body weight is not more than 10%. The improved key point detection algorithm has high detection accuracy and inference speed, which provides accurate yield data for pelagic fishing and is expected to solve the existing statistical problems and improve the accuracy and real-time performance of data in the fishing industry.

Accurate building energy consumption prediction is essential for efficient energy management and energy optimization. This study utilizes bidirectional long short-term memory (BiLSTM) to automatically extract deep time series features. The nonlinear fitting and high-precision prediction capabilities of Random Forest (RF) and XGBoost models are then utilized to develop a BiLSTM-RF-XGBoost stacked hybrid model. To enhance model generalization and reduce overfitting, a Bayesian algorithm with an early stopping mechanism is utilized to fine-tune hyperparameters, and strict K-fold time series cross-validation (TSCV) is implemented for performance evaluation. The hybrid model achieves a high TSCV average R² value of 0.989 during cross-validation. When evaluated on an independent test set, it yields a mean square error (MSE) of 0.00003, a root mean square error (RMSE) of 0.00548, a mean absolute error (MAE) of 0.00130, and a mean absolute percentage error (MAPE) of 0.26%. These values are significantly lower than those of comparison models, indicating a significant improvement in predictive performance. The study offers insights into the internal decision-making of the model through SHAP (SHapley Additive exPlanations) feature significance analysis, revealing the key roles of temperature and power lag features, and validating that the stacked model effectively utilizes the outputs of base models as meta-features. This study makes contributions by proposing a novel hybrid model trained with Bayesian optimization, analyzing the influence of various feature factors, and providing innovative technological solutions for building energy consumption prediction. It also provides theoretical value and guidance for low-carbon building energy management and application.

Due to the vulnerability and high risk of the ship environment, the Ship Information System (SIS) should provide 24 hours of uninterrupted protection against network attacks. Therefore, the corresponding intrusion detection mechanism is proposed for this situation. Based on the collaborative control structure of SIS, this paper proposes an anomaly detection pattern based on risk data analysis. An intrusion detection method based on the critical state is proposed, and the corresponding analysis algorithm is given. In the Industrial State Modeling Language (ISML), risk data are determined by all relevant data, even in different subsystems. In order to verify the attack recognition effect of the intrusion detection mechanism, this paper takes the course/roll collaborative control task as an example to carry out simulation verification of the effectiveness of the intrusion detection mechanism.

In order to adapt to the development of the industrial Internet of Things, the relationship between the internal components of electromechanical equipment is getting closer and closer, such as motor bearings. Nowadays, timely diagnosis of motor bearing faults is urgently needed. Most traditional methods for motor bearing fault diagnosis use a single learner and emphasize the role of feature extraction, which usually requires a large amount of sample support and computer runtime to obtain satisfactory performance. In this article, the Bayesian optimized decision tree with ensemble classifiers after feature extraction of the original data is finally proposed which has good performance. We use multiple feature extraction to establish the feature matrix and construct a decision tree model with the ensemble method for AdaBoost and a Bayesian optimized decision tree model with ensemble classifiers to conduct experiments on the accuracy, prediction speed, etc., of the model. We derived four sets of experimental data. The results show that the optimal method is the Bayesian optimized decision tree with ensemble classifiers after feature extraction. The accuracy of this method is as high as 99.9. At the same time, unlike previous studies, we found in our study that feature extraction does not improve the accuracy of diagnosis for the decision tree with ensemble method for AdaBoost and there is a precipitous decline. In the industrial Internet of Things, the conclusion can improve certain reference value for the future fault diagnosis of motor bearings.

At present, catch statistics in the pelagic fishery industry rely mainly on manual counting methods. However, this method suffers from low statistical accuracy and insufficient timeliness. An automatic tuna counting approach based on ByteTrack and YOLOv7-Tuna is presented in this research. The method selects YOLOv7 as the base model, adopts DySnakeConv to obtain more temporal features, combines it with CoordConv to enhance the location-awareness ability of the model, and introduces DyHead to suppress the interference of complex backgrounds. The experimental results show that YOLOv7-Tuna outperforms YOLOv7 in terms of precision by 5.2%, recall by 3.1%, mAP@0.5 by 0.5%, and mAP@0.5:0.95 by 10%. Furthermore, the ByteTrack algorithm was employed to achieve real-time tracking of targets, with specific counting areas added. The results indicate that the counting error of this method decreased to 3.1%. It can effectively accomplish automatic counting tasks for tuna, providing a new solution for the automatic quantification of catch in the offshore fishing industry.