With the recent increase in the utilization of logistics and courier services, it is time for research on logistics systems fused with the fourth industry sector. Algorithm studies related to object recognition have been actively conducted in convergence with the emerging artificial intelligence field, but so far, algorithms suitable for automatic unloading devices that need to identify a number of unstructured cargoes require further development. In this study, the object recognition algorithm of the automatic loading device for cargo was selected as the subject of the study, and a cargo object recognition algorithm applicable to the automatic loading device is proposed to improve the amorphous cargo identification performance. The fuzzy convergence algorithm is an algorithm that applies Fuzzy C Means to existing algorithm forms that fuse YOLO(You Only Look Once) and Mask R-CNN(Regions with Convolutional Neuron Networks). Experiments conducted using the fuzzy convergence algorithm showed an average of 33 FPS(Frames Per Second) and a recognition rate of 95%. In addition, there were significant improvements in the range of actual box recognition. The results of this study can contribute to improving the performance of identifying amorphous cargoes in automatic loading devices.

This paper is on the suggestion of maintenance items for electric railway facility systems. With the recent increase in the use of electric locomotives, the utilization and importance of railroad electrical facility systems are also increasing, but the railroad electrical facility system in Korea is rapidly aging. To solve this problem, various methodologies are applied to ensure operational reliability and stability for railroad electrical facility systems, but there is a lack of detailed evaluation criteria for railroad electrical facility system maintenance. Also, maintenance items must be selected in a scientific and systematic method. Therefore, railroad electrical facility systems are selected for study. Design Structure Matrix (DSM) is utilized to establish considerations tailored to the maintenance characteristics, and the Fuzzy-TOPSIS methodology is utilized for determining the maintenance detail evaluation item baseline weights, a multi-criteria decision-making problem. Studies show that degradation, insulation items have the highest weight of 14.63%, and capacity items have the lowest weight of 5.34%. The results of this may be contributed to the underlying research in carrying out maintenance activities to ensure the reliability and safety of railroad electrical facility systems.

Human Adaptive Mechatronics (HAM) includes human and computer system in a closed loop. Elderly person with disabilities, normally carry out their daily routines with some assistance to move their limbs. With the short fall of human care takers, mechatronics devices are used with the likes of exoskeleton and exosuits to assist them. The rehabilitation and occupational therapy equipments utilize the electromyography (EMG) signals to measure the muscle activity potential. This paper focuses on optimizing the HAM model in prediction of intended motion of upper limb with high accuracy and to increase the response time of the system. Limb characteristics extraction from EMG signal and prediction of optimal controller parameters are modeled. Time and frequency based approach of EMG signal are considered for feature extraction. The models used for estimating motion and muscle parameters from EMG signal for carrying out limb movement predictions are validated. Based on the extracted features, optimal parameters are selected by Modified Lion Optimization (MLO) for controlling the HAM system. Finally, supervised machine learning makes predictions at different points in time for individual sensing using Support Vector Neural Network (SVNN). This model is also evaluated based on optimal parameters of motion estimation and the accuracy level along with different optimization models for various upper limb movements. The proposed model of human adaptive controller predicts the limb movement by 96% accuracy.