Search

Forest regeneration by means of seedlings grown in container nurseries is usually performed manually with the use of the standard dibble bar or the tube dibble. Manual placement of a large number of seedlings in the soil requires a lot of work. Manual removal of the soil cover and digging the soil in spots with a diameter of 0.4 m requires, under average conditions, about 38 man-hours/ha, while planting with a dibble bar requires about 34 man-hours/ha. Additional work time is needed to carry seedlings over an area that is being afforested. At present, forestry does not have automatic planters that would enable the establishment of forest cultures. The aim of the paper is to present the concept of an autonomous robot and an innovative technology of performing forest regeneration and afforestation of former agricultural and reclaimed areas. The paper also presents the design solutions of the key working unit, which is a universal, openable dibble, cooperating with a three-toothed shaft to prepare a planting spot. The solution proposed enables continuous operation of the machine, i.e. without the need to stop the base vehicle.

[EN] Electric motors use about 68% of total generated electricity. Fault diagnosis of electrical motors is an important task, because it allows saving a large amount of money and time. An analysis of acoustic signals is a promising tool to improve the accuracy of fault diagnosis. It is essential to analyze acoustic signals to assess the state of the motor. In this paper, three electric impact drills (EID) were analyzed using acoustic signals: healthy EID, EID with damaged rear bearing, EID with damaged front bearing. Three angle grinders (AG) were analyzed: healthy AG, AG with 1 blocked air inlet, AG with 2 blocked air inlets. The authors proposed a method for feature extraction: SMOFS-NFC (Shortened Method of Frequencies Selection Nearest Frequency Components). Acoustic features vectors were classified by the nearest neighbor classifier and Naive Bayes classifier. The classification accuracy were in the range of 89.33¿97.33% for three electric impact drills. The classification accuracy were in the range of 90.66¿100% for three angle grinders. The presented method is very useful for diagnosis of bearings, ventilation faults and other mechanical faults of power tools. It can be also useful for diagnosis of similar power tools., This work was supported in part by Generalitat Valenciana, Conselleria de Innovacion, Universidades, ' Ciencia y Sociedad Digital, (project AICO/019/224).

COVID-19 is a profoundly contagious pandemic that has taken the world by storm and has afflicted different fields of life with negative effects. It has had a substantial impact on education which is evident from the transition from Face-to-Face (F2F) teaching to online mode of education and the rigid implementation of lockdown across the globe. This study examines the factors impacting the performance of teachers during the lockdown period of COVID-19 using various feature selection algorithms and Artificial Intelligence techniques. In this paper, we have proposed a novel multilevel feature selection for the prediction of the factors affecting the teachers’ satisfaction with online teaching and learning in COVID-19. The proposed multilevel feature selection is composed of the Fast Correlation Based Filter (FCBF), Mutual Information (MI), Relieff, and Particle Swarm Optimization (PSO) feature selection. The performance of the proposed feature selection approach is evaluated through the teachers’ benchmark dataset. We used a range of measures like accuracy, precision, f-measure, and recall to evaluate the performance of the proposed approach. We applied different machine learning approaches (SVM, LGBM, and ANN) with the proposed multilevel feature selection technique. The performance of the proposed approach is also compared with existing feature selection algorithms, and the results show the improvement in the performance of feature selection in terms of accuracy, precision, recall, and F-Measure. Proposed feature selection provides more than 80% accuracy with Light Weight Gradient Boosting Machine (LGBM).

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

Many visually meaningful image encryption (VMIE) methods have been proposed in the literature using reference encryption. However, the most important problems of these methods are low visual quality and blindness. Owing to the low visual quality, the pre-encrypted image can be analyzed simply from the reference image and, in order to decrypt nonblind methods, users should use original reference images. In this paper, two novel reference image encryption methods based on the integer DWT (discrete wavelet transform) using 2 k correction are proposed. These methods are blind and have high visual quality, as well as short execution times. The main aim of the proposed methods is to solve the problem of the three VMIE methods existing in the literature. The proposed methods mainly consist of the integer DWT, pre-encrypted image embedding by kLSBs (k least significant bits) and 2 k correction. In the decryption phase, the integer DWT and pre-encrypted image extraction with the mod operator are used. Peak signal-to-noise ratio (PSNR) measures the performances of the proposed methods. Experimental results clearly illustrate that the proposed methods improve the visual quality of the reference image encryption methods. Overall, 2 k correction and kLSBs provide high visual quality and blindness.

Segmentation of retinal vessels plays a crucial role in detecting many eye diseases, and its reliable computerized implementation is becoming essential for automated retinal disease screening systems. A large number of retinal vessel segmentation algorithms are available, but these methods improve accuracy levels. Their sensitivity remains low due to the lack of proper segmentation of low contrast vessels, and this low contrast requires more attention in this segmentation process. In this paper, we have proposed new preprocessing steps for the precise extraction of retinal blood vessels. These proposed preprocessing steps are also tested on other existing algorithms to observe their impact. There are two steps to our suggested module for segmenting retinal blood vessels. The first step involves implementing and validating the preprocessing module. The second step applies these preprocessing stages to our proposed binarization steps to extract retinal blood vessels. The proposed preprocessing phase uses the traditional image-processing method to provide a much-improved segmented vessel image. Our binarization steps contained the image coherence technique for the retinal blood vessels. The proposed method gives good performance on a database accessible to the public named DRIVE and STARE. The novelty of this proposed method is that it is an unsupervised method and offers an accuracy of around 96% and sensitivity of 81% while outperforming existing approaches. Due to new tactics at each step of the proposed process, this blood vessel segmentation application is suitable for computer analysis of retinal images, such as automated screening for the early diagnosis of eye disease.

In this study, a multistage segmentation technique is proposed that identifies cancerous cells in prostate tissue samples. The benign areas of the tissue are distinguished from the cancerous regions using the texture of glands. The texture is modeled based on wavelet packet features along with sample entropy values. In a multistage segmentation process, the mean-shift algorithm is applied on the pre-processed images to perform a coarse segmentation of the tissue. Wavelet packets are employed in the second stage to obtain fine details of the structured shape of glands. Finally, the texture of the gland is modeled by the sample entropy values, which identifies epithelial regions from stroma patches. Although there are three stages of the proposed algorithm, the computation is fast as wavelet packet features and sample entropy values perform robust modeling for the required regions of interest. A comparative analysis with other state-of-the-art texture segmentation techniques is presented and dice ratios are computed for the comparison. It has been observed that our algorithm not only outperforms other techniques, but, by introducing sample entropy features, identification of cancerous regions of tissues is achieved with 90% classification accuracy, which shows the robustness of the proposed algorithm.

This paper discusses various challenges in remote welding with a surgical robot equipped with a digital camera used to observe the welding zone, in particular the difficulty in detecting the boundaries of the weld pool. The difference in the processing of the real image by the human brain is discussed in comparison with the image in the form of a film from a digital camera. In addition to the need of performing the second derivative of the image in real-time, three models of human recognition of an image were discussed, one of which was already studied by researchers from Cambridge, UK. The concept of melting the base material by bending the weld pool with the pressure of non-ionized arc gases and the American implementation of the measurement of the third dimension of the weld pool and determining the weld penetration by electronics of the welding machine are discussed. Desired movement trajectories of the electrode tip based on the physics of the welding arc and welding technology are presented along with difficulties in teaching the movements to welding trainees. Basics of the neural model of the brain with the vector model of artificial intelligence are also presented.

Mathematical models have been applied in prediction of growth hormone treatment effectiveness in children since the end of 1990s. Usually they were multiple linear regression models; however, there are also examples derived by empirical non-linear methods. Proposed solution consists in application of machine learning technique – artificial neural networks – to analyse this problem. This new methodology, contrary to previous ones, allows detection of both linear and non-linear dependencies without assuming their charactera priori. The aims of this work included: development of models predicting separately growth during 1st year of treatment and final height as well as identification of important predictors and in-depth analysis of their influence on treatment’s effectiveness. The models were derived on the basis of clinical data of 272 patients treated for at least 1 year, 133 of whom have already attained final height. Starting from models containing 17 and 20 potential predictors, respectively for 1st year and final height model, we were able to reduce their number to 9 and 10. Basing on the final models, IGF-I concentration and earlier growth were indicated as belonging to most important predictors of response to GH therapy, while results of GH secretion tests were automatically excluded as insignificant. Moreover, majority of the dependencies were observed to be non-linear, thus using neural networks seems to be reasonable approach despite it being more complex than previously applied methods.

Automatic detection of voice pathologies enables non-invasive, low cost and objective assessments of the presence of disorders, as well as accelerating and improving the process of diagnosis and clinical treatment given to patients. In this work, a vector made up of 28 acoustic parameters is evaluated using principal component analysis (PCA), kernel principal component analysis (kPCA) and an auto-associative neural network (NLPCA) in four kinds of pathology detection (hyperfunctional dysphonia, functional dysphonia, laryngitis, vocal cord paralysis) using the a, i and u vowels, spoken at a high, low and normal pitch. The results indicate that the kPCA and NLPCA methods can be considered a step towards pathology detection of the vocal folds. The results show that such an approach provides acceptable results for this purpose, with the best efficiency levels of around 100%. The study brings the most commonly used approaches to speech signal processing together and leads to a comparison of the machine learning methods determining the health status of the patient

In the paper, the problem of extraction of complex decision rules in simple decision systems over ontological graphs is considered. The extracted rules are consistent with the dominance principle similar to that applied in the dominancebased rough set approach (DRSA). In our study, we propose to use a heuristic algorithm, utilizing the ant-based clustering approach, searching the semantic spaces of concepts presented by means of ontological graphs. Concepts included in the semantic spaces are values of attributes describing objects in simple decision systems

Ground-penetrating radar (GPR) is commonly used for locating burial sites. In this article, we acquired radargrams at a site where a domestic pig cadaver was buried. The measurements were conducted with the ProEx System GPR manufactured by the Swedish company Mala Geoscience with an antenna of 500MHz. The event corresponding to the pig can be clearly seen in the measurements. In order to improve the interpretation, the electromagnetic field is compared to numerical simulations computed with the pseudo-spectral Fourier method. A geological model has been defined on the basis of assumed electromagnetic properties (permittivity, conductivity and magnetic permeability). The results, when compared with the GPR measurements, show a dissimilar amplitude behaviour, with a stronger reflection event from the bottom of the pit. We have therefore performed another simulation by decreasing the electrical conductivity of the body very close to that of air. The comparison improved, showing more reflections, which could be an indication that the body contains air or has been degraded to a certain extent that the electrical resistivity has greatly increased.

W artykule przedstawiono mozliwości zastąpienia maski spawalniczej z tradycyjnym filtrem optycznym na maske spawalniczą wyposazoną w komputerowy system przetwarzania przestrzennego obrazu, umozliwiającą lepszą obserwacje procesu spawania poprzez wyeliminowanie olśnienia wzroku spawacza, ulatwienie rozpoznawania granicy cieklego jeziorka i poprawe widoczności krawedzi rowka. Ponadto wykorzystanie techniki rzeczywistości wzbogaconej pozwoli na przedstawianie dodatkowych informacji wspomagających proces spawania. W pracy omowiono metody analizy jakości plomienia i luku spawalniczego, a takze metody wykrywania granicy jeziorka oraz określenia glebokości wtopienia. Omowiono rowniez nowoczesne metody filtracji obrazu pozyskanego z kamery cyfrowej oraz mozliwości ich zastosowania w innowacyjnej masce spawalniczej z komputerowym przetwarzaniem obrazu. The possibility of creating a welding mask with computer processing of spatial image instead of welding filters The article presents possibility of replacing of the welding mask with a traditional optic filter by a welding mask with a computer spacial image processing system, which enables a better observation of the welding process through the elimination of the eyes blinding, facilitation the recognition of the welding pool boundary, and improvement of the back bead edge visibility. Moreover, utilization of an augmented reality technic will allows to present the additional information supporting the welding process. This work discusses methods for the analysis of flame and welding arc as well as for the recognition of pool boundary and penetration depth. Furthermore, the article presents modern methods for a filtration of images collected from a digital camera as well as opportunities of their application in a building of the innovative welding mask with the computer analysis of 3D image.

Part 4: Data Analysis and Information Retrieval; International audience; Creators of numerous information systems frequently concentrate on the semantic aspects of communication during planning and forming of man-machine interactions. Meanwhile emotions and adequate reactions to needs play equally essential role as rational reasoning when intelligence of a partner is judged. Intelligent behaviours and reactions usually deliberately affect the partner’s needs. Therefore a computer could be accepted as an intelligent partner if it considers human needs affecting emotions. This paper presents a new method of automatic human needs recognition based on the extended personality typology that is described using characteristic verbal expressions. It enables to perform automatic passive classification of personality by means of psycholinguistic analysis during typical merit man-machine communication.

The application of computer modelling for medical purposes, although challenging, is a promising pathway for further development in the medical sciences. We present predictive neural and k-nearest neighbour (k-NN) models for hearing improvements after middle ear surgery for chronic otitis media. The studied data set comprised 150 patients characterised by the set of input variables: age, gender, preoperative audiometric results, ear pathology and details of the surgical procedure. The predicted (output) variable was the postoperative hearing threshold. The best neural models developed in this study achieved 84% correct predictions for the test data set while the k-NN model produced only 75.8% correct predictions.

The most important task that could improve the efficacy of managing the prostate cancer (PCa) is to develop the technique which will be able to detect an existing PCa even in cases when currently used methods are insufficient. It is supposed that the perfusion computed tomography technology (p-CT) can improve the diagnosis of early PCa. Unfortunately, the perfusion prostate images are very difficult to analyze especially for doctors who are not enough experienced with such a kind of images. Therefore there is a need to find a computational method which could help the doctors to make the decision whether the prostate cancer exists or not and (if the results are positive) to correctly point out the cancerous region. In research which results are presented in the paper we analyzed a great number of prostate images derived from over 50 patients with proven or suspected PCa. We propose the new method, named "life-belt" which has significant potential for identifying cancerous regions.