Development and evaluation of a smartphone-based system for inspection of road maintenance work

Abstract. In the road construction industry, doing work inspection is a laborious and resource-consuming job because of the distributed work site. Contractors in Finland require to capture photos of every road fix they have done as proof of their work. It is well-established that with the help of smartphone technology, these kinds of manual work can be reduced. This thesis aims to develop and evaluate a smartphone-based system to capture video evidence of task completion. The system, designed and developed in this thesis, consists of an Android application named ’Road Recorder’ and a web tool for managing the content collected by Road Recorder. While mounted to a vehicle’s dashboard used in construction work, the Road Recorder can record the videos of road surface and geo-location information and some other metadata and send them to a remote server that is inspected using the web tool. Users of different backgrounds were given the system to accomplish some tasks and were observed closely. The users were interviewed at the end, and responses were analyzed to find the usability of the applications. The results indicate the high usability of the Road Recorder application and reveal possible improvements for the Road Recorder management web application. Overall, Road Recorder is a great step towards the automation of such construction work inspection. Though there were some limitations in the evaluation process, it demonstrates that Road Recorder is easy to use and can be a useful tool in the industry

Fruit Quality Assessment with Densely Connected Convolutional Neural Network

Accurate recognition of food items along with quality assessment is of paramount importance in the agricultural industry. Such automated systems can speed up the wheel of the food processing sector and save tons of manual labor. In this connection, the recent advancement of Deep learning-based architectures has introduced a wide variety of solutions offering remarkable performance in several classification tasks. In this work, we have exploited the concept of Densely Connected Convolutional Neural Networks (DenseNets) for fruit quality assessment. The feature propagation towards the deeper layers has enabled the network to tackle the vanishing gradient problems and ensured the reuse of features to learn meaningful insights. Evaluating on a dataset of 19,526 images containing six fruits having three quality grades for each, the proposed pipeline achieved a remarkable accuracy of 99.67%. The robustness of the model was further tested for fruit classification and quality assessment tasks where the model produced a similar performance, which makes it suitable for real-life applications.Comment: Accepted in 12th ICECE (4 pages, 3 Figures, 3 Tables

An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification

Correct identification and categorization of plant diseases are crucial for ensuring the safety of the global food supply and the overall financial success of stakeholders. In this regard, a wide range of solutions has been made available by introducing deep learning-based classification systems for different staple crops. Despite being one of the most important commercial crops in many parts of the globe, research proposing a smart solution for automatically classifying apple leaf diseases remains relatively unexplored. This study presents a technique for identifying apple leaf diseases based on transfer learning. The system extracts features using a pretrained EfficientNetV2S architecture and passes to a classifier block for effective prediction. The class imbalance issues are tackled by utilizing runtime data augmentation. The effect of various hyperparameters, such as input resolution, learning rate, number of epochs, etc., has been investigated carefully. The competence of the proposed pipeline has been evaluated on the apple leaf disease subset from the publicly available `PlantVillage' dataset, where it achieved an accuracy of 99.21%, outperforming the existing works.Comment: Accepted in ECCE 2023, 6 pages, 6 figures, 4 table

MAC-Assisted topology control for ad-hoc wireless networks

We consider ad-hoc wireless networks and the topology control problem whose objective is to minimize the amount of power needed to maintain connectivity. The issue boils down to selecting the optimum transmission power level at each node based on the position information of reachable nodes. Local decisions regarding the transmission power level induce a subgraph of the maximum powered graph $G_{max}$ in which edges represent direct reachability at maximum power. We propose a new algorithm for constructing minimum-energy path-preserving subgraphs of $G_{max}$, i.e., ones minimizing the energy consumption between node pairs. Our algorithm involves a modification to the Medium Access Control (MAC) layer. Its superiority over previous solutions, up to $60\%$ improvement in sparse networks, demonstrates once again that strict protocol layering in wireless networks tends to be detrimental to performance

Artificial Intelligence Model to Predict Surface Roughness of Ti-15-3 Alloy in EDM Process

Conventionally the selection of parameters depends intensely on the operator’s experience or conservative technological data provided by the EDM equipment manufacturers that assign inconsistent machining performance. The parameter settings given by the manufacturers are only relevant with common steel grades. A single parameter change influences the process in a complex way. Hence, the present research proposes artificial neural network (ANN) models for the prediction of surface roughness on first commenced Ti-15-3 alloy in electrical discharge machining (EDM) process. The proposed models use peak current, pulse on time, pulse off time and servo voltage as input parameters. Multilayer perceptron (MLP) with three hidden layer feedforward networks are applied. An assessment is carried out with the models of distinct hidden layer. Training of the models is performed with data from an extensive series of experiments utilizing copper electrode as positive polarity. The predictions based on the above developed models have been verified with another set of experiments and are found to be in good agreement with the experimental results. Beside this they can be exercised as precious tools for the process planning for EDM

RSM model to evaluate material removal rate in EDM of Ti-5Al-2.5Sn using graphite electrode

The usage of electrical discharge machining (EDM) is increasing gradually owing to its capability to cut precisely, geometrically complex material regardless hardness. Many process parameters greatly affect the EDM performance and complicated mechanism of the process result the lag of established theory. Hence, it becomes important to select the proper parameter set for different machining stages in order to promote efficiency. In view of these barriers, it is attempted to establish a model which can accurately predict the material removal rate (MRR) of titanium alloy by correlating the process parameter. Effect of the parameters on MRR is investigated as well. Experiment is conducted utilizing the graphite electrode maintaining negative polarity. Analysis and modelling is carried out based on design of experiment as well as response surface methodology. The agreeable accuracy is obtained and thus the model can become a precise tool setting the EDM process cost effective and efficient. Moreover, high ampere, short pulse-off time and low servo-voltage combined with about 250 μs pulse-on time generate the highest MRR