How good are ideas identified by an automatic idea detection system?

This paper describes an automatic oil leak detection system which can detect accidental oil leaks at an early stage by continuously and remotely monitoring the underground tank that stores oil for standby engine generators. The engine generator is a regular power supply of an emergency backup power supply for telecommunication facilities. The system is made up of a liquid level meter, an oil leak detection sensor and measurement/monitoring equipment for measuring the amount of oil and monitoring the leak. The liquid level meter measures the amount of oil stored in the tank. Oil leaks from the tank are automatically detected by the oil leak detection sensor. The measurement/monitoring equipment displays the amount of oil on a LCD, and regularly as well as upon demand forwards this information to the remote monitoring center. Moreover, this equipment displays the warnings on both the LCD and a LED, and forwards warnings such as oil leaks to the remote monitoring center. The introduction of this system will help detect accidental oil leaks at an early stage. This system is expected to reduce or eliminate the need for maintenance checks, greatly reducing working hours and expenditures required for the maintenance of the underground tanks.

The EU funded Iliad project builds on the assets resulting from two decades of investments in policies and infrastructures for the blue economy aiming to establish an interoperable, data-intensive, and cost-effective Digital Twin of the Ocean (DTO). Iliad DTO combines ocean observations, state-of-the-art forecast models, citizen science, AI and advanced computing infrastructures (cloud computing, HPC, Internet of Things, Big Data, social networking, and more) to create high-resolution, multi-variable and multi-dimensional near real-time virtual representations of the ocean. Continuously integrating data from numerous in-situ sensors and satellites, Iliad DTO can provide a platform for researchers, policy makers, the industry, and citizens, to monitor the impact of climate change and human activities on ocean health and productivity and make data driven management decisions for a sustainable blue economy. Several Digital Twin (DT) pilots will be undertaken in key thematic areas such as offshore wind energy, wave and tidal energy, biodiversity assessments, fisheries and aquaculture, marine pollution and more. The current work presents the Oil Spill Response Digital Twin pilot developed in the frame of Iliad. The DT focuses on Cretan Sea and aims to provide early detection of marine oil spills and operational forecasting of spill trajectories to support immediate response to pollution events, minimizing thus the impact on marine ecosystems, coastal communities and the economy and reducing the time for environmental recovery. A multi-model approach is followed for predicting the fate and transport of oil spills, employing MEDSLIK-II and OpenDrift particle tracking models, coupled to operational, high-resolution numerical weather (WRF), hydrodynamic (NEMO) and sea state (WAVEWATCH III) models for Cretan Sea. Real-time observations (current speed/direction, sea water temperature, wave height) from novel, low-cost on-line sensors are integrated in the DT and assimilated into the operational metocean forecasting chain for validation purposes and for improving models’ forecasting skills. A near-real time automatic oil spill detection system, from Sentinel-1 SAR images, is developed, which allows early detection of marine oil spills and triggers the oil spill forecasting system, producing accurate short-term forecasting of spills’ trajectories and fate. Automatic detection and classification of oil spill events employs a trained FCOS which performs initial object detection, fine-tuned with a dataset of +1000 SAR images, including 4 different classes (oil spill, look-alike, ship, land). Image pre-processing and oil spill mask delineation is performed using SNAPpy-based (Sentinel Application Platform Python toolbox) adaptive thresholding algorithms. The oil spill detection and forecasting system is tested by reconstructing the Ulysse-Virginia oil spill, which occurred off the coast of Corsica on October 7th, 2018. Sentinel-1 images are used to detect and delineate the coverage of the spill, to initiate Medslik-II and OpenDrift models for simulating the oil spill fate and transport. Oil spill predictions are produced using different metocean forcings, highlighting the importance of high-resolution metocean data in accurate forecasting of oil spill trajectories. Quantitative metrics are used to evaluate the ability of the oil spill models to reproduce the satellite oil spill observations.    Acknowledgement: This research has received funding from the European Union’s H2020 RIA programme under GA No 101037643.

The data of a wafer with defects can provide engineers with very important information and clues to improve the yield rate and quality in manufacturing. This paper presents a microscope automatic positioning and wafer detection system with human-machine interface based on image processing and fuzzy inference algorithms. In the proposed system, a XY table is used to move the position of each die on 6 inch or 8 inch wafers. Then, a high-resolution CCD and one set of two-axis optical linear encoder are used to accurately measure the position on the wafer. Finally, the developed human-machine interface is used to display the current position of an actual wafer in order to complete automatic positioning, and a wafer map database can be created. In the process of defect detection, CCD is used for image processing, and during preprocessing, it is required to filter noise, acquire the defect characteristics, define the defective template, and then take the characteristic points of the defective template as the reference input for fuzzy inference. A high-accuracy optical automatic positioning and wafer defect detection system is thus constructed. This study focused on automatic detection of spots, scratches, and bruises, and attempted to reduce the time to detect defective die and improve the accuracy of determining the defects of semiconductor devices.

Media bias and the intolerance of media outlets and citizens to deal with opposing points of view pose a threat to the proper functioning of democratic processes. In this respect, we present a systematic review of the literature related to media bias detection, in order to characterize and classify the different types of media bias, and to explore the state-of-the-art of automatic media bias detection systems. The main objectives of this paper were twofold. First, we framed information, misinformation and disinformation within a theoretical framework that allows us to differentiate the different existing misinformation problems such as us media bias, fake news, or propaganda. Second, we studied the state of the art of automatic media bias detection systems: analyzing the most recently used techniques and their results, listing the available resources and the most relevant datasets, and establishing a discussion about how to increase the maturity of this area. After doing a comprehensive literature review, we have identified and selected a total of 17 forms of media bias that can be classified depending on the context (e.g., coverage bias, gatekeeping bias, or statement bias), and on the author’s intention (e.g., spin bias, or ideology bias). We also reviewed, following the PRISMA methodology, the main automatic media bias detection systems that have been developed so far, selecting 63 relevant articles, from which we extracted the most used techniques; including non-deep learning methods (e.g., linguistic-based methods, and reported speech-based methods), and deep learning methods (e.g., RNNs-based methods, and transformers-based methods). Additionally, we listed and summarized 18 available datasets for the task of automatic media bias detection. In conclusion, the current methods for automatic media bias detection are still in their infancy and there is still a lot of potential for improvement in terms of accuracy and robustness. We have proposed some future research lines that could potentially contribute to the development of more advanced techniques.

The ferrite magnetic tiles are widely used in industry field. At present, the defects detection in ferrite magnetic tile surfaces is done by manual work. In order to improve the defects detection efficiency and prevent missed and false detection, an automatic detection system applied to the magnetic tiles non-destructive detection was proposed based on computer vision. A suit of the automatic defects detection equipment used for magnetic tile surfaces was designed so that we can adjust the position and angle of the lightings and cameras programmatically to meet the requirement of different location for different kinds of magnetic tiles. To solve the problem of automatically detect defects from magnetic tile images which are with dark colors and low contrasts, a new hybrid algorithm which combines nonsubsampled Contourlet transform and Laws texture feature measurement was proposed to eliminate the influence of the grinding textures and extract defects. In this methodology the original image was first decomposed by nonsubsampled Contourlet transform, the characteristics of the decomposition coefficients are analyzed by Laws texture feature measurement. Then according to the texture energies of the restructured image, a segmentation threshold was determined to reset the decomposition coefficients. Finally the image was reconstructed with the reconstruction coefficients, the grinding textures were eliminated and defects were obtained by Canny operator. The experimental results show that based on the proposed method, the grinding textures can be eliminated effectively, the defects can be extracted accurately, and the accuracy rate of extraction defects can achieve 93.57%. The automatic detection system can provide an effective solution to magnetic tile defects detection industry.

Purpose Diagnosing the crop diseases by farmers accurately with the naked eye can be challenging. Timely identification and treating these diseases is crucial to prevent complete destruction of the crops. To overcome these challenges, in this work a light-weight automatic crop disease detection system has been developed, which uses novel combination of residual network (ResNet)-based feature extractor and machine learning algorithm based classifier over a real-time crop dataset. Design/methodology/approach The proposed system is divided into four phases: image acquisition and preprocessing, data augmentation, feature extraction and classification. In the first phase, data have been collected using a drone in real time, and preprocessing has been performed to improve the images. In the second phase, four data augmentation techniques have been applied to increase the size of the real-time dataset. In the third phase, feature extraction has been done using two deep convolutional neural network (DCNN)-based models, individually, ResNet49 and ResNet41. In the last phase, four machine learning classifiers random forest (RF), support vector machine (SVM), logistic regression (LR) and eXtreme gradient boosting (XGBoost) have been employed, one by one. Findings These proposed systems have been trained and tested using our own real-time dataset that consists of healthy and unhealthy leaves for six crops such as corn, grapes, okara, mango, plum and lemon. The proposed combination of Resnet49-SVM and ResNet41-SVM has achieved accuracy of 99 and 97%, respectively, for the images that have been collected from the city of Kurukshetra, India. Originality/value The proposed system makes novel contribution by using a newly proposed real time dataset that has been collected with the help of a drone. The collected image data has been augmented using scaling, rotation, flipping and brightness techniques. The work uses a novel combination of machine learning methods based classification with ResNet49 and ResNet41 based feature extraction.

Bearded seal vocalizations were recorded by four spatially separated receivers on the Chukchi Continental Slope in Alaska in 2016–2017. Bearded seals vocalizations are often analyzed manually or by using automatic detections that are manually validated. An automatic detection and classification system (DCS) based on convolutional neural networks (CNNs) is proposed. The DCS is divided into two sections. First, regions of interest (ROI) containing potential bearded seal calls are detected through a 2D normalized cross-correlation of the measured spectrogram and a representative template of the two calls of interest considered in this work. Second, CNNs are used to classify the ROIs to determine if they are noise or a specified vocalization. The CNNs are trained on 80% of the ROIs manually labeled from one of the recorders and validated on the remaining 20% with a classification accuracy above 95.5%. When assessing the generalization performance of the DCS, we tested on the remaining three recorders located at different positions, obtaining a precision above 89.2% for the main class of the two types of calls. This study provides evidence that CNNs are suitable for classifying bearded seal vocalizations from ROIs found by classic detection techniques. [Work supported by ONR via Grant No. N00014-18-1-2140.]

This paper presents the development of automatic vehicle plate detection system using image processing technique. The famous name for this system is Automatic Number Plate Recognition (ANPR). Automatic vehicle plate detection system is commonly used in field of safety and security systems especially in car parking area. Beside the safety aspect, this system is applied to monitor road traffic such as the speed of vehicle and identification of the vehicle's owner. This system is designed to assist the authorities in identifying the stolen vehicle not only for car but motorcycle as well. In this system, the Optical Character Recognition (OCR) technique was the prominent technique employed by researchers to analyse image of vehicle plate. The limitation of this technique was the incapability of the technique to convert text or data accurately. Besides, the characters, the background and the size of the vehicle plate are varied from one country to other country. Hence, this project proposes a combination of image processing technique and OCR to obtain the accurate vehicle plate recognition for vehicle in Malaysia. The outcome of this study is the system capable to detect characters and numbers of vehicle plate in different backgrounds (black and white) accurately. This study also involves the development of Graphical User Interface (GUI) to ease user in recognizing the characters and numbers in the vehicle or license plates.

Abstract. The Institute of Seismology of University of Helsinki is building a new local seismic network, called OBF network, around planned nuclear power plant in Northern Ostrobothnia, Finland. The network will consist of nine new stations and one existing station. The network should be dense enough to provide azimuthal coverage better than 180° and automatic detection capability down to ML −0.1 within a radius of 25 km from the site.The network construction work began in 2012 and the first four stations started operation at the end of May 2013. We applied an automatic seismic signal detection and event location system to a network of 13 stations consisting of the four new stations and the nearest stations of Finnish and Swedish national seismic networks. Between the end of May and December 2013 the network detected 214 events inside the predefined area of 50 km radius surrounding the planned nuclear power plant site. Of those detections, 120 were identified as spurious events. A total of 74 events were associated with known quarries and mining areas. The average location error, calculated as a difference between the announced location from environment authorities and companies and the automatic location, was 2.9 km. During the same time period eight earthquakes between magnitude range 0.1–1.0 occurred within the area. Of these seven could be automatically detected. The results from the phase 1 stations of the OBF network indicates that the planned network can achieve its goals.

Accurate detection of lameness in dairy cattle with computer vision: A new and individualized detection strategy based on the analysis of the supporting phase

In this paper proposed a system for automatic defects detection in images. The solution to this problem is widely used in practice. Automatic detection is found in the challenge of detecting defects on the road surface, in the textile industry, as well as virtual restoration of archival photo images. The solution to this range of problems allows speeding up work in these areas, and in some cases, completely solving. To solve the first two problems (search for defects on the pavement and textiles), it is enough to create a mask that localizes defects in the image with maximum reliability, while photo restoration requires additional algorithms to restore the detected damaged areas. The proposed method is based on the latest achievements in the field of machine learning and allows solve the main disadvantages of traditional methods. Automatic defect detection is performed using a neural network with compound descriptor. A series of experiments confirmed the high efficiency of the proposed method in comparison with traditional methods for detecting defects.

Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals

Robust real-time ship detection and tracking for visual images automatically has become one of the crucial requirements for numerous situations. In order to improve the performance of automatic ship target detection and tracking system, a novel method based on mean shift is proposed for automatic detection and tracking of ship in the corrected video sequences. Firstly, video frames are corrected based on real-time attitude of the imaging equipment in order to decrease the drift of the ship target from frame to frame. Secondly, sea horizon is extracted based on Ostu algorithm and Hough transform. Thirdly, the ship location is detected based on the detection of grayscale peak. Finally, the ship is automatically tracked by using the mean shift algorithm. The result shows that this method has not only improved the robustness of the system against the shaking while tracking the ships, but also achieved a high success rate of tracking ships automatically in corrected video sequences.

Study on an Automatic Ship Detection System and Its Information Display

Avalanche occurrences are unambiguous indicators of unstable snow conditions. Information on past and current avalanche activity is therefore crucial for avalanche forecasting. To continuously assess avalanche activity, automatic detection systems are required. In recent years, technological and signal processing advances have led to the development of operational infrasound avalanche detection systems. We evaluated the detection performance of four operationally running infrasound detection systems installed at three different sites in the Swiss Alps during two entire winter seasons. To this end, we collected a comprehensive data set of avalanche activity using a network of automatic cameras and supplementary field observations by local observers. The events automatically identified by the systems were then compared to the data set of visually observed avalanches. Only 3% of the 839 observed avalanches were associated with automatic detections and 21% of the automatic detections were confirmed by field observations. However, the majority of observed avalanches were small and most automatic detections occurred during periods of poor visibility. Furthermore, the probability of detection (POD) increased with avalanche size, decreased with distance, and varied with avalanche type. Dry-snow avalanches were generally better detected than wet-snow and mixed-type avalanches. Large avalanches (on the order of 300 m wide and 1000 m long) within a distance of 3 km from the array were typically well detected (POD ≈ 90%). The false alarm ratio was estimated to 13–30%.