In the last few decades, photovoltaic (PV) power station installations have surged across the globe. The output efficiency of these stations deteriorates with the passage of time due to multiple factors such as hotspots, shaded cell or module, short-circuited bypass diodes, etc. Traditionally, technicians inspect each solar panel in a PV power station using infrared thermography to ensure consistent output efficiency. With the advancement of drone technology, researchers have proposed to use drones equipped with thermal cameras for PV power station monitoring. However, most of these drone-based approaches require technicians to manually control the drone which in itself is a cumbersome task in the case of large PV power stations. To tackle this issue, this study presents an autonomous drone-based solution. The drone is mounted with both RGB (Red, Green, Blue) and thermal cameras. The proposed system can automatically detect and estimate the exact location of faulty PV modules among hundreds or thousands of PV modules in the power station. In addition, we propose an automatic drone flight path planning algorithm which eliminates the requirement of manual drone control. The system also utilizes an image processing algorithm to process RGB and thermal images for fault detection. The system was evaluated on a 1-MW solar power plant located in Suncheon, South Korea. The experimental results demonstrate the effectiveness of our solution.

Automatic license plate recognition (ALPR) is generally considered a solved problem in the computer vision community. However, most of the current works on ALPR are designed to work on license plates (LP) from specific countries and use country-specific information which limits their practical applicability. Such ALPR systems require changes in the algorithm to work on other countries’ LPs. Previous works on multinational LP recognition are tested on datasets from various countries that share the same LP layout. To address this issue, this study presents a deep ALPR system designed to be applicable to multinational LPs. The proposed approach consists of three main steps – LP detection, unified character recognition, and multinational LP layout detection. The system is mainly based on the you only look once (YOLO) networks. Particularly, tiny YOLOv3 was used for the first step whereas the second step uses YOLOv3-SPP – a version of YOLOv3 that consists of the spatial pyramid pooling (SPP) block. The localized LP is fed into YOLOv3-SPP for character recognition. The character recognition network returns the bounding boxes of the predicted characters and does not provide information about the sequence of the LP number. A LP number with an incorrect sequence is considered wrong. Thus, to extract the correct sequence, we propose a layout detection algorithm that can extract the correct sequence of LP numbers from multinational LPs.We collected our own Korean car plate (KarPlate) dataset and made it publicly available. The proposed system was evaluated on LP datasets from five countries which include South Korea, Taiwan, Greece, USA, and Croatia. In addition, a small dataset containing LPs from 17 countries was collected to evaluate the effectiveness of the multinational LP layout detection algorithm. The proposed ALPR system consumes about 42 ms per image on average for extracting LP number. Experimental results demonstrate the effectiveness of our ALPR system.

Increasing man-machine trust has burgeoned during the last few decades. The growing interest in trust-building has led to the study of the non-dichotomous nature of trust. Trust as social behavior is an integral part of effective team building. The major focus has been offered to study how humans build trust towards machines, whereas few attempts have been made to study the reverse. Studies have shown that trustworthiness perceptions initialize trust behavior whereas trust behavior influences subsequent trustworthiness perceptions. This paper presents the design and comparative analysis of evidential fuzzy multi-criteria decision-making (EFMCDM) based on multi-dimensional trust quantification schemes to quantify trust level with the human agent in a collaborative environment.