Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

Drones are becoming increasingly popular not only for recreational purposes but also in a variety of applications in engineering, disaster management, logistics, securing airports, and others. In addition to their useful applications, an alarming concern regarding physical infrastructure security, safety, and surveillance at airports has arisen due to the potential of their use in malicious activities. In recent years, there have been many reports of the unauthorized use of various types of drones at airports and the disruption of airline operations. To address this problem, this study proposes a novel deep learning-based method for the efficient detection and recognition of two types of drones and birds. Evaluation of the proposed approach with the prepared image dataset demonstrates better efficiency compared to existing detection systems in the literature. Furthermore, drones are often confused with birds because of their physical and behavioral similarity. The proposed method is not only able to detect the presence or absence of drones in an area but also to recognize and distinguish between two types of drones, as well as distinguish them from birds. The dataset used in this work to train the network consists of 10,000 visible images containing two types of drones as multirotors, helicopters, and also birds. The proposed deep learning method can directly detect and recognize two types of drones and distinguish them from birds with an accuracy of 83%, mAP of 84%, and IoU of 81%. The values of average recall, average accuracy, and average F1-score were also reported as 84%, 83%, and 83%, respectively, in three classes.

Optimization-Based Business Process Model Matching

The rapid increase in generation of business process models in the industry has raised the demand on the development of process model matching approaches. In this paper, we introduce a novel optimization-based business process model matching approach which can flexibly incorporate both the behavioral and label information of processes for the identification of correspondences between activities. Given two business process models, we achieve our goal by defining an integer linear program which maximizes the label similarities among process activities and the behavioral similarity between the process models. Our approach enables the user to determine the importance of the local label-based similarities and the global behavioral similarity of the models by offering the utilization of a predefined weighting parameter, allowing for flexibility. Moreover, extensive experimental evaluation performed on three real-world datasets points out the high accuracy of our proposal, outperforming the state of the art.

Measuring Behavioral Similarity of Cellular Automata

Conway's Game of Life is the best-known cellular automaton. It is a classic model of emergence and self-organization, it is Turing-complete, and it can simulate a universal constructor. The Game of Life belongs to the set of semi-totalistic cellular automata, a family with 262,144 members. Many of these automata may deserve as much attention as the Game of Life, if not more. The challenge we address here is to provide a structure for organizing this large family, to make it easier to find interesting automata, and to understand the relations between automata. Packard and Wolfram (1985) divided the family into four classes, based on the observed behaviors of the rules. Eppstein (2010) proposed an alternative four-class system, based on the forms of the rules. Instead of a class-based organization, we propose a continuous high-dimensional vector space, where each automaton is represented by a point in the space. The distance between two automata in this space corresponds to the differences in their behavioral characteristics. Nearest neighbors in the space have similar behaviors. This space should make it easier for researchers to see the structure of the family of semi-totalistic rules and to find the hidden gems in the family.

A comparison of regionalization methods in monsoon dominated tropical river basins

The present study evaluated five regionalization methods: global averaging; regression; spatial proximity; behavioral similarity and artificial neural network (ANN) for Soil and Water Assessment Tool (SWAT), using data from 24 river basins in monsoon dominated tropical river basins of peninsular India. Regionalization was performed for each basin using the remaining 23 basins. The performance of the calibration and thus the regionalization method is limited by the unreliable or erroneous data at the basins. Overall, we found that the regression method outperforms other regionalization methods in terms of predicting the daily as well as peak discharges. It was found that despite showing a better R2 in training, testing and validation, the ANN method performed poorly probably due to a lower number of training data. Therefore, it is suggested that the ANN should be avoided for regionalization in the absence of sufficient training data. Moreover, the regression equations developed in the present study can be utilized to predict SWAT parameters of basins located in the vicinity of the study area. However, the basins located far away from the group of catchments or having diverse characteristics should be avoided for regionalization.

Common Process Extraction pada Model Proses Bisnis Tebang Muat Angkut (TMA)

Abstract— In an organizational environment, there are various business process models with the same procedures. If an organization builds a system with the same procedure repeatedly, it will undoubtedly incur a lot of effort and money. Therefore, it is necessary to extract common fragments to save effort. This research uses four scenarios of business process models: sequence, branching, nested branching, and looping. This study uses Business Process Modeling Notation (BPMN) notation so that the process model consists of activities, connectors, and gateways. Structural similarity is measured using the Jaccard similarity formula by comparing the process model. The similarity of behavior is measured using the Transition Adjacency Relations (TARs) method to obtain common fragments. The results show that the sequence process model will produce a common fragment that tends to be sequential too. The branching will produce a common fragment that tends to branch, and nested branching will produce a common fragment that tends to be branched and nested, as well as looping will produce a common fragment contains looping too. The experimental results show that the proposed method can extract common fragments based on the available business process models. Keywords—BPMN; common fragment; behavioral similarity; TARs Abstrak— Dalam lingkungan organisasi, terdapat berbagai model proses bisnis dengan prosedur yang sama. Jika suatu organisasi membangun sistem dengan prosedur yang sama secara berulang-ulang, niscaya akan mengeluarkan banyak tenaga dan biaya. Oleh karena itu, perlu mengekstrak fragmen umum untuk menghemat tenaga. Penelitian ini menggunakan empat skenario model proses bisnis yaitu sequence, branching, nested branching, dan looping. Penelitian ini menggunakan notasi Business Process Modeling Notation (BPMN) sehingga model proses terdiri dari aktivitas, konektor, dan gateway. Kemiripan struktural diukur menggunakan rumus kemiripan Jaccard dengan membandingkan model proses. Kesamaan perilaku diukur menggunakan metode Transition Adjacency Relations (TARs) untuk mendapatkan fragmen yang sama. Hasil penelitian menunjukkan bahwa model sequence process akan menghasilkan common fragment yang cenderung berurutan juga. Percabangan akan menghasilkan fragmen umum yang cenderung bercabang, dan percabangan bersarang akan menghasilkan fragmen umum yang cenderung bercabang dan bersarang, serta perulangan akan menghasilkan fragmen umum yang berisi perulangan juga. Hasil eksperimen menunjukkan bahwa metode yang diusulkan dapat mengekstrak fragmen umum berdasarkan model proses bisnis yang tersedia. Keywords—BPMN; common fragment; kemiripan perilaku; TARs