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RESUMO Objetivo: As atividades que os profissionais Bibliotecários desempenham estão se adaptando às novas formas de comunicação e às demandas modernas dos usuários. Dentre essas atividades, tem-se a indexação. O presente artigo possui o objetivo de descrever o panorama de produção científica e as tendências de pesquisa sobre os estudos voltados para a indexação automática, semiautomática e auxiliada por computador, indexada na Base de Dados Referencial de Artigos de Periódicos em Ciência da Informação (Brapci), no período de no período de 1972 a 2021. Método: Caracteriza-se como exploratória e bibliográfica com fins descritivos, uma vez que descreve e detalha a produção científica sobre os temas “indexação automática”, “indexação semiautomática” e “indexação auxiliada por máquina” indexada na Brapci. Resultado: Por meio da análise desse corpus, foi possível identificar os pesquisadores que mais produziram sobre a temática em questão, bem como os periódicos que mais tratam da referida temática, além disso, perceberam-se os períodos de tempo mais fecundos à produção de trabalhos relacionados com a automação da indexação e as palavras-chave utilizadas à indexação do corpus analisado, as quais, juntamente com os resumos, sinalizaram as tendências de estudos nessa vertente. Conclusões: Há uma tendência de estudos voltados à indexação totalmente automática, dando ênfase aos métodos semânticos e não apenas estatísticos, fazendo uso de recursos advindos da Inteligência Artificial. Poucas propostas de indexação semiautomática foram evidenciadas. Propostas promissoras na prática da representação temática da informação de forma automática em curtos espaços de tempo se apresentam hodiernamente. ABSTRACT Objective: The activities that professional Librarians perform are adapting to new forms of communication and the modern demands of users. Among these activities, there is indexing. This article aims to describe the panorama of scientific production and research trends on studies aimed at automatic, semi-automatic and computer-aided indexing, indexed in the Reference Database for Journal Articles in Information Science (Brapci), in the period from 1972 to 2021. Methods: It is characterized as exploratory and bibliographical with descriptive purposes, since it describes and details the scientific production on the topics "automatic indexing", "semi-automatic indexing" and "machine-aided indexing" indexed in Brapci. Results: Through the analysis of this corpus, it was possible to identify the researchers who produced the most on the subject in question, as well as the journals that most deal with that subject, in addition, the most fruitful periods of time for the production of related works with the automation of indexing and the keywords used to index the analyzed corpus, which, together with the abstracts, signaled the trends of studies in this area. Conclusions: There is a tendency for studies aimed at fully automatic indexing, emphasizing semantic and not just statistical methods, making use of resources derived from Artificial Intelligence. Few proposals for semi-automatic indexing were evidenced. Promising proposals in the practice of thematic representation of information automatically in short spaces of time are presented today.

Seabed inspection is one of the most sought-after applications for Autonomous Underwater Vehicles (AUVs). In particular, acoustic sensors, as Side Scan Sonars (SSSs) and Forward-Looking Sonars (FLSs), are commonly favored over optical cameras to carry out such a task being not influenced by the illumination conditions and providing high-range data. However, acoustic images are often hard to interpret with conventional automatic techniques, forcing human operators to analyze thousands of collected images to identify the so-called Objects of Potential Interest (OPIs). In this perspective, this paper reports the development of an Automatic Target Recognition (ATR) methodology to identify and localize OPIs in FLS imagery; such detections have been then exploited to realize a world model with the Probabilistic Multiple Hypothesis Anchoring (PMHA) data association algorithm. Both the ATR and world modeling systems were on-field tested at the Naval Support and Experimentation Centre (Centro di Supporto e Sperimentazione Navale - CSSN) basin, in La Spezia, Italy, in a multi-vehicle architecture by employing an acoustic channel between FeelHippo AUV and an autonomous moving buoy.

In this paper, we build and compare multiple speech systems for the automatic evaluation of the severity of a speech impairment due to oral cancer, based on spontaneous speech. To be able to build and evaluate such systems, we collected a new spontaneous oral cancer speech corpus from YouTube consisting of 124 utterances rated by 100 non-expert listeners and one trained speech-language pathologist, which we made publicly available. We evaluated the systems in two scenarios: a scenario where transcriptions were available (reference-based) and a scenario where transcriptions might not be available (reference-free). The results of extensive experiments showed that (1) when transcriptions were available, the highest correlation with the human severity ratings was obtained using an automatic speech recognition (ASR) retrained with oral cancer speech. (2) When transcriptions were not available, the best results were achieved by a LASSO model using modulation spectrum features. (3) We found that naive listeners’ ratings are highly similar to the speech pathologist's ratings for speech severity evaluation. (4) The use of binary labels led to lower correlations of the automatic methods with the human ratings than using severity scores.

Speech intelligibility is an essential though complex construct for evaluating dysarthric speech. Various procedures can be used to measure speech intelligibility, most of which are based on subjective ratings assigned by experts. Since these procedures are subjective and laborious, automatic speech recognition (ASR) has been proposed to obtain objective metrics of intelligibility. Although promising results have been reported, ASR for dysarthric speech generally requires large amounts of data consisting of recorded and annotated speech. In the present study, we explored the possibility of using dysarthric speech resources from the dominant language variety to improve the performance of ASR systems on the dysarthric speech of the non-dominant variety of the same pluricentric language. Dutch is used as an example of a pluricentric language, with Netherlandic Dutch considered the dominant and Flemish Dutch the non-dominant variety. The performance of ASR is evaluated by using two types of intelligibility metrics: orthographic transcriptions and global intelligibility assessments, both obtained from experts. Overall, the results show that dysarthric speech data from the dominant language variety can contribute to improving automatic transcriptions and to developing objective, automatic global measures of speech intelligibility only when no data from the non-dominant variety are available for training ASR models.

The need for accurate and up-to-date spatial data by decision-makers in public and private administrations is increasing gradually. In recent decades, in the management of disasters and smart cities, fast and accurate extraction of roads, especially in emergencies, is quite important in terms of transportation, logistics planning, and route determination. In this study, automatic road extraction analyses were carried out using the Unmanned Aerial Vehicle (UAV) data set, belonging to the Yildiz Technical University Davutpasa Campus road route. For this purpose, this paper presents a comparison between performance analysis of rule-based classification and U-Net deep learning method for solving automatic road extraction problems. Objects belonging to the road and road network were obtained with the rule-based classification method with overall accuracy of 95%, and with the deep learning method with an overall accuracy of 86%. On the other hand, the performance metrics including accuracy, recall, precision, and F1 score were utilized to evaluate the performance analysis of the two methods. These values were obtained from confusion matrices for 4 target classes consisting of road and road elements namely road, road line, sidewalk, and bicycle road. Finally, integration of classified image objects with ontology was realized. Ontology was developed by defining four target class results obtained as a result of the rule-based classification method, conceptual class definition and properties, rules, and axioms.

The so-called ‘mismatch’ is a factor which experts in the forensic voice comparison field encounter regularly. Therefore, we decided to explore to what extent the automatic-speaker-recognition system’s and the earwitness’ ability to identify speakers is influenced when recordings are acquired in different languages and at different times. 100 voices in a database of 300 recordings (100 speakers recorded in three mutually mismatched sessions) were compared with an automatic-speaker-recognition software VOCALISE based on i-vectors and x-vectors, and by 39 respondents in simulated voice parades. Both the automatic and perceptual approach seem to have yielded similar results in that the less complex the mismatch type, the more successful the identification. The results point to the superiority of the x-vector approach, and also to varying identification abilities of listeners.

This article deals with model predictive control (MPC) design for automatic transmission (AT) upshift inertia phase, which aims to optimally coordinate the actions of oncoming (ONC) and off- going (OFG) clutches and engine and to facilitate calibration. The designed MPC strategy accounts for clutch actuation dynamics and constraints, while setting the tradeoff between three key and conflicting shift quality criteria: comfort ; duration ; and efficiency. The shift comfort and duration are ensured by minimizing output shaft torque and ONC clutch slip speed tracking errors, and the shift efficiency is reflected in clutch energy loss minimization on a prediction horizon. This allows for the calibration of the MPC performance through setting the inertia phase duration, the output shaft torque reference, cost function weighting coefficients, and constraints, rather than optimizing the shift control profiles themselves. The MPC problem is formulated as a constrained quadratic programming problem and efficiently solved online by an interior-point solver. The proposed MPC strategy is applicable to other transmissions with multiple actuators, such as parallel hybrid transmissions. The MPC system is examined through nonlinear powertrain model simulations for one to three shift and its performance is compared with an offline, multiobjective optimization-based control strategy. The MPC design flexibility and ease of calibration are demonstrated for different shift comfort and duration targets, as well as cost function tuning, and robustness with respect to clutch actuation parameter uncertainties is examined.

LLC resonant converters are used in renewable energy applications to achieve high power efficiency conversions between different energy sources, buses, and energy storage elements. The traditional design methods for LLC resonant converters are based on a simple frequency-domain analysis (FDA). However, the accuracy of FDA is not satisfactory, especially at wide voltage range applications. To overcome the drawbacks of the traditional FDA method in wide-range applications, in this article, the time-domain analysis (TDA) is adopted to achieve accurate analysis of LLC converters. Efficiency is normally selected as the optimization objective, where the circuit components and accurate power loss models are required beforehand. To achieve a more general optimal design method for LLC converters, a simple root-mean-square (rms) current and TDA-based optimal design method is proposed. The optimal design is achieved by minimizing the converter rms current and ensuring some key design considerations. Moreover, an automatic design tool for the proposed rms-current-based optimal design method is developed, and advanced optimization algorithms are introduced to improve the optimization speed. A 2.5-kW experimental prototype is built using the optimal circuit parameters. Experimental results under different operating conditions are demonstrated, and the results are consistent with the theoretical analysis. Efficiency comparisons between the proposed optimal design method and conventional design method are made. The proposed optimal design method can improve the converter efficiency by a maximum value of 2.14%.

The applicability of machine learning (ML) techniques has recently been expanding to include automatic real estate valuation models. The main advantage of this technique is that it can better capture complexity in the value determination process. Therefore, the performance of these techniques is shown to be superior to conventional models. In this paper, the latest ML algorithms (i.e., support vector machine, random forest, XGBoost, LightGBM, and CatBoost algorithms) are examined as automatic valuation models, and several combination methods are proposed to improve the models’ predictive power. We applied ML models to approximately 57,000 records on apartment transactions, which were provided by South Korea’s Ministry of Land, Infrastructure, and Transport, that occurred in Seoul in 2018. The results are as follows. First, ML-based predictors (especially, the latest decision tree-based algorithms) are more performative than conventional models. Second, the prediction error from a model can be partially offset by another model’s error, which implies that an efficient averaging of the predictors improves their predictive accuracy. Third, the models’ relative performance may be relearned by the ML algorithms, which means that they can also be used to recommend which algorithm should be selected for making predictions.

The problem of determining the components of the structural-automatic model is solved by creating a graph of states and transitions. The purpose of the process is to form a formalized expression of the object of study in the form of a structural-machine model of the process of firing a mobile armored vehicle with a series of three shots with fragmentation-beam projectiles of directional action. This model can be further practically implemented during the development of the latest samples of fire resistance in order to reduce errors at the stage of system design. This approach reduces the cost of design and production of prototypes by up to 25%. This paper considers the process involving interrelated elements of the components of a system while accounting for all possible variants of its behavior from the moment of detection to the failure of a single target in armor protection to perform tasks as intended. The execution of a fire task is considered as a set of certain procedures characterized by the average value of their duration. The explosive destruction of the hull of each fragmentation-beam projectile is characterized by the self-propagation of the reaction of explosive transformations based on tabular data on an armored combat vehicle. Appropriate procedures (phases) of firing a single target in armor protection are advisable to formalize to create preconditions for obtaining the value of a statistical indicator of the effectiveness of causing damage to the target and to study further alternative options for this process. For the proposed structural-automatic model of the process of firing a single target in armor protection with a series of fragmentation-beam projectiles of directional action, validation and verification were carried out, which demonstrated the convergence of the results that exceeded 60%. The use of the structural-automatic model's components proposed in this work increases the probability of performing a fire task for the first shot from 0.23 to 0.88, for the second – from 0.35 to 0.95, for the third – from 0.45 to 0.98

International Journal of Intelligent Engineering and Systems is an OPEN ACCESS international journal An Automatic Generation Control (AGC) is considered a substantial stage in power systems to ensure that the area-frequency response and the tie-line power of an interchanging steady state errors are acceptable values especially at the transient state. Whiles, several techniques have been designed for the AGC, Fuzzy logic control is commonly used. However, it is faced a longer processing time issues to adjust the accurate single in the transient state. In this paper, a Single Input Fuzzy Logic Gain Scheduling PID Controller (SIFL-PID) is designed as a supplementary loop for AGC of two area interconnected power system to reduce the processing time. The lower number of rules and the simple parameters of the SIFL-PID will contribute to minimise the design time required for the Fuzzy Logic Gain Scheduling FL-PID. The SIFL-PID also reduced the signed distance technique when it is generated one input variable simple mathematical model of the system. In comparison with the FL-PID, the number of rules controlling system can be significantly reduced. The main outcome of this work is that it is assessed the effectiveness of the SIFL-PID for two power systems that are interconnected when it is compared with the FL-PID using the MATLAB/SIMULINK environment. The results prove that, the simple design of the SIFL-PID control is validated under various condition tests. Performance analysis standards are also performed. The results of all simulations demonstrate that the suggested controller provides better performances such as like as minimum Performance analysis standards, frequency deviation power deviations and low overshoot and undershoot

In order to prevent the illegal export of paintings abroad, a museum examination using various methods for studying a work of art is carried out. At the same time, an analysis is also made of historical, art history, financial and other information and documents confirming the painting’s authenticity — provenance. Automation of such examination is hampered by the need to take into account numerical values of visual features, quality indicators, and verbal descriptions from provenance. In this paper, we consider the problem of automatic multi-task classification of paintings for museum expertise. A system architecture is proposed that checks provenance, implements a fine-grained image analysis (FGIA) of visual image features, and automatically classifies a painting by authorship, genre, and time of creation. Provenance is contained in a knowledge graph; for its vectorization, it is proposed to use a graph2vec type encoder with an attention mechanism. Fine-grained image analysis is proposed to be performed using searching discriminative regions (SDR) and learning discriminative regions (LDR) allocated by convolutional neural networks. To train the classifier, a generalized loss function is proposed. A data set is also proposed, including provenance and images of paintings by European and Ukrainian artists. Для запобігання незаконному вивезенню картин за кордон прово- диться музейна експертиза з використанням різних методів дослідження твору мистецтва, зокрема аналіз історичних, мистецтвознавчих, фінансових та інших відомостей і документів, що підтверджують справжність картин – провенансу. Автоматизація такої експертизи ускладнюється необхідністю враховувати числові значення візуальних ознак, показників якості та словесні описи з провенансу. Розглянуто завдання автоматичної багатозадачної класифікації картин під час музейної експертизи. Запропоновано архітектуру системи, яка перевіряє провенанс, реалізує детальний аналіз (FGIA) візуальних ознак зображення та виконує автоматичну класифікацію картини за авторством, жанром та часом створення. Провенанс міститься у графі знань, для векторизації якого запропоновано використовувати енкодер типу graph2vec з механізмом уваги, а детальний аналіз пропонується виконувати за допомогою пошукових відмітних регіонів (SDR) та навчальних відмітних регіонів (LDR), що виділяються згортковими нейронними мережами. Для навчання класифікатора запропоновано узагальнену функцію втрат, а також набір даних, що включає провенанс та зображення картин європейських та українських художників.

We investigated the relationship between the head size and radiation dose in pediatric brain computed tomography (CT) to evaluate the validity of automatic exposure control (AEC). Phantom experiments were performed to assess image noise with and without AEC, and indicated that AEC decreased differences in noise between slices of different section sizes. Retrospective analysis was conducted on 980 pediatric brain CT scans where the tube current was determined using AEC. The water equivalent diameter (WED) was employed as an index of the head size, and mean WED for each image set (WEDmean) and WED for each slice (WEDslice) were used for analysis. For the image-set-based analysis, volume CT dose index (CTDIvol) was compared to WEDmean. For the slice-based analysis, the tube current was compared to WEDslice using 20 of the 980 sets. Additionally, CTDIvol and WEDmean were compared between male and female patients matched for age, weight, or WEDmean. CTDIvol increased with increasing WEDmean, and an exponential curve was closely fitted to the relationship. Tube current changed similarly to the change in WEDslice for each image set, and an exponential curve was well-fitted to the plots of tube current against WEDslice when data from the 20 sets were pooled together. Although CTDIvol and WEDmean were slightly but significantly larger for male than female patients after matching for age or weight, a sex-dependent difference in CTDIvol was not found after matching for WEDmean. This study indicated successful dose modulation using AEC according to the head size for each patient and each slice location. The application of AEC to pediatric brain CT is recommended for radiation dose optimization.

Using automatic identification system (AIS) data, this article first has extended the definition of three widely used roadway congestion indices to maritime transportation systems (MTS), traffic speed index (TSI), traffic rate index (TRI), and dwell time index (DTI). Next, a methodology is developed to measure the indices based on AIS data, considering various factors, including path geometry, time of day, and the type and size of vessels, and finally the method has been applied to the AIS data of the Houston Ship Channel (HSC) to evaluate the applicability in real cases. The results show that although average TSI and TRI cannot represent waterway congestion, the real-time values (rather than the average) at the micro level can help finding location, time, and severity of traffic congestion. Besides, while TSI and TRI have shortcomings, both average and real-time dwell time index (DTI) can quantify traffic congestion and highlight severity in different waterway segments for different types of vessels. When congestion happens at some narrow waterways, vessels need to wait at sea buoy or docks, thus dwell time index (DTI) can quantify traffic congestion better than in-transit indices such as travel speed, TSI. According to HSC DTI, most tankers experience long waiting times at the sea buoy and Galveston Bay, while cargo vessels experience delays at Bayport and Barbour's Cut terminals. This paper helps the decision-makers quantify congestion in different sections of a waterway and provides measures to compare congestion for national competing projects at different waterways.

Reading comprehension is one of the essential skills for students as they make a transition from learning to read to reading to learn. Over the last decade, the increased use of digital learning materials for promoting literacy skills (e.g., oral fluency and reading comprehension) in K-12 classrooms has been a boon for teachers. However, instant access to reading materials, as well as relevant assessment tools for evaluating students’ comprehension skills, remains to be a problem. Teachers must spend many hours looking for suitable materials for their students because high-quality reading materials and assessments are primarily available through commercial literacy programs and websites. This study proposes a promising solution to this problem by employing an artificial intelligence (AI) approach. We demonstrate how to use advanced language models (e.g., OpenAI’s GPT-2 and Google’s T5) to automatically generate reading passages and items. Our preliminary findings suggest that with additional training and fine-tuning, open-source language models could be used to support the instruction and assessment of reading comprehension skills in the classroom. For both automatic story and item generation, the language models performed reasonably; however, the outcomes of these language models still require a human evaluation and further adjustments before sharing them with students. Practical implications of the findings and future research directions are discussed.

Plants need water to grow well, so watering must follow the plant's needs. Parameters that affect the process of watering plants are air temperature and soil moisture, thus, we need a control system that can make decisions in the form of watering time to meet plant water needs according to their needs, namely an automatic watering system. Fuzzy method can be used for human-like decision making in a control system. This study uses the Sugeno fuzzy method which is integrated into the Microcontroller as the controller. Testing of this system is carried out on eggplant plants which are regulated based on how long the watering time is so that the air temperature and soil moisture are following the plant's needs. The test resulted that the effective watering time interval so that the eggplant soil moisture was maintained at 40-60% was 21 seconds. It can be concluded that the fuzzy inference embedded in the design has been able to overcome the problem of watering eggplant plants, where the test shows that after the watering process, the soil moisture value can be maintained at an average value of 55% so that the soil moisture is following the needs of the plant.

Kampung Gadog Desa Sukamahi merupakan sebuah kampung yang terletak di Cianjur Jawa Barat. Topologi Kampung Gadog dikelilingi oleh persawahan sehingga mayoritas masyarakat berprofesi sebagai petani. Pendapatan masyarakat yang rendah mengakibatkan kondisi masyarakat kurang sejahtera. Untuk membantu masyarakat meningkatkan perekonomiannya dan melihat potensi air yang melimpah serta menerapkan IPTEK di kampung Gadog, maka tim akan membangun kolam budidaya ikan lele. Yang perlu diperhatikan untuk budidaya ikan lele adalah waktu pemberian pakan tepat waktu, sedangkan masyarakat menghabiskan waktu seharian jika bercocok tanam di sawah / di ladang karena posisi yang jauh. Untuk memfasilitasi hal tersebut maka pemberian pakan akan menggunakan automatic fish feeder yang sudah dilengkapi dengan photovoltaic di bagian atas alat.. Sedangkan penerapan IPTEK menggunakan pompa air DC berbasis energi surya, dimana pompa air ini akan digunakan untuk proses pengairan kolam. Untuk fish feeder dalam pemberian pakan dapat di setting sesuai dengan kebutuhan, dimana dalam hal ini settingan waktu yang dibuat adalah dalam setiap 5 jam sekali. Kolam yang digunakan adalah kolam konvensional dengan ukuran panjang x lebar x tinggi adalah 3m x 2m x1m, dengan jumlah 4 unit kolam yang terbagi menjadi 1 kolam untuk pembibitan, 1 kolam untuk pertumbuhan dan 2 kolam lainnya untuk kolam panen. Dan untuk pompa air yang digunakan untuk menguras dan atau mengisi kembali air ke kolam digunakan pompa Air DC dengan kapasitas output pompa sebesar 180 Watt ,12 Volt. Untuk panen ikan di lakukan setelah ikan berusia 3 bulan dari sejak pembibitan pada usia 15 hari.

Low-power consumption and long-distance transmission are two problems that have to be solved by the application of broadband power line communication for the automatic meter reading system. To reduce the power consumption of the communication module, based on the analysis of the composition of the power consumption, some methods are proposed. From the communication chip level and the module circuit level, the design scheme of low-power consumption is given. To solve the problem of transmission distance, a frequency band of 2.44 MHz~5.6 MHz is used as the main working frequency band. The communication module supports multiple frequency bands. Using this feature, the optimal frequency band is adaptively selected for communication and automatic switching, which further improve the transmission distance. Field application shows that the above methods effectively decrease the power consumption of the communication module and extend the transmission distance.

In a cluster of news texts on the same event, two sentences from different documents might express different multi-document phenomena (redundancy, complementarity, and contradiction). Cross-Document Structure Theory (CST) provides labels to explicitly represent these phenomena. The automatic identification of the multi-document phenomena and their correspondent CST relations is definitely handy for Automatic Multi-Document Summarization since it helps computers understand text meaning. In this paper, we evaluated a typology of (textual) signals for the automatic detection of the CST relations of complementarity (i.e., Historical background, Follow-up and Elaboration) in a multi-document corpus of news texts in Brazilian Portuguese. Using algorithms from different machine-learning paradigms, we obtained classifiers that achieved high general accuracy (higher than 90%), indicating the potential of the signals. Em uma coleção de notícias sobre um mesmo evento, duas sentenças de textos distintos podem expressar diferentes fenômenos multidocumento (redundância, complementaridade e contradição). A Cross-Document Structure Theory (CST) provê rótulos para representar esses fenômenos. A identificação automática dos fenômenos multidocumento e das relações CST correspondentes é central à Sumarização Automática Mutidocumento, pois ajuda a máquina a entender o conteúdo textual. Neste artigo, avaliou-se uma tipologia de sinais (textuais) para a detecção automática das relações CST de complementaridade (Historical background, Follow-up e Elaboration) em um corpus multidocumento de notícias em Português do Brasil. Utilizando algoritmos de diferentes paradigmas de Aprendizado de Máquina, obtiveram-se classificadores que atingiram alto índice de acurácia geral (superior a 90%), indicando o potencial dos sinais.

Recent advances in optical remote sensing, especially with the development of machine learning models have made it possible to automatically classify different crop types based on their unique spectral characteristics. In this article, a simple feed-forward artificial neural network (ANN) was implemented for the automatic classification of various crop types. A DJI Mavic air drone was used to simultaneously collect about 549 images of a mixed-crop farmland belonging to Federal University of Technology Minna, Nigeria. The images were annotated and the ANN algorithm was implemented using custom-designed Python programming scripts with libraries such as NumPy, Label box, and Segmentation Mask, for the classification. The algorithm was designed to automatically classify maize, rice, soya beans, groundnut, yam and a non-crop feature into different land spectral classes. The model training performance, using 70% of the dataset, shows that the loss curve flattened down with minimal over-fitting, showing that the model was improving as it trained. Finally, the accuracy of the automatic crop-type classification was evaluated with the aid of the recorded loss function and confusion matrix, and the result shows that the implemented ANN gave an overall training classification accuracy of 87.7% from the model and an overall accuracy of 0.9393 as computed from the confusion matrix, which attests to the robustness of ANN when implemented on high-resolution image data for automatic classification of crop types in a mixed farmland. The overall accuracy, including the user accuracy, proved that only a few images were incorrectly classified, which demonstrated that the errors of omission and commission were minimal.

Annotated datasets for automatic white balance (AWB) are used for the evaluation and, when necessary, the training, of AWB methods. Relying on such datasets requires awareness of the potential bias in their content and characteristics: some methods are designed to rely on the presence of particular elements, such as human skin, while other methods learn implicit relationships between image content and light properties from training data. The dependency on these relationships makes it fundamental to understand whether the available datasets are actually representative of common application scenarios, such as the presence of human subjects, the diversity of composition, or the illumination conditions. In this paper we overview the most common datasets for Automatic White Balance, including those for single as well as multiple illuminant estimation, providing a critical analysis on their characteristics. Furthermore, we identify a number of existing methods for single illuminant estimation, as a representative pool of approaches to the problem with various levels of complexity. We investigate how the performance of these correlate to the image content of common datasets.

Background: Black-box skepticism is one of the main hindrances impeding deep-learning-based automatic sleep scoring from being used in clinical environments. Methods: Towards interpretability, this work proposes a sequence-to-sequence sleep-staging model, namely SleepTransformer. It is based on the transformer backbone and offers interpretability of the model's decisions at both the epoch and sequence level. We further propose a simple yet efficient method to quantify uncertainty in the model's decisions. The method, which is based on entropy, can serve as a metric for deferring low-confidence epochs to a human expert for further inspection. Results: Making sense of the transformer's self-attention scores for interpretability, at the epoch level, the attention scores are encoded as a heat map to highlight sleep-relevant features captured from the input EEG signal. At the sequence level, the attention scores are visualized as the influence of different neighboring epochs in an input sequence (i.e. the context) to recognition of a target epoch, mimicking the way manual scoring is done by human experts. Conclusion: Additionally, we demonstrate that SleepTransformer performs on par with existing methods on two databases of different sizes. Significance: Equipped with interpretability and the ability of uncertainty quantification, SleepTransformer holds promise for being integrated into clinical settings., This article has been published in IEEE Transactions on Biomedical Engineering

Many studies have been conducted using obstacle hazard values, called potential method, for connected autonomous vehicle. However, most studies were conducted for static obstacles, and those for dynamic obstacles assumed an environment without oncoming or crossing vehicles. In this study, we devise an algorithm for generating potential values considering time series characteristics using information that can be obtained through inter-vehicle communication and propose a path ?nding algorithm that uses these potential values. As an evaluation of the usefulness of the proposed method, we compare it with existing potential methods. The results show that, in some situations, the route derived by the proposed method is superior to the route derived by the existing potential method in terms of safety and timer to reach the destination.

Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed choices can be a very challenging and time-consuming task. This paper seeks to help modellers by leveraging the Bayesian framework and the concept of automatic relevance determination (ARD), in order to automatically determine an optimal utility function specification from an exponentially large set of possible specifications in a purely data-driven manner. Based on recent advances in approximate Bayesian inference, a doubly stochastic variational inference is developed, which allows the proposed DCM-ARD model to scale to very large and high-dimensional datasets. Using semi-artificial choice data, the proposed approach is shown to very accurately recover the true utility function specifications that govern the observed choices. Moreover, when applied to real choice data, DCM-ARD is shown to be able discover high quality specifications that can outperform previous ones from the literature according to multiple criteria, thereby demonstrating its practical applicability., 21 pages, 2 figures, 11 tables

Over time, traditional blackboards have been gradually replaced by whiteboards in schools and other educational institutions due to the convenience of cleaning. With the increasing popularity of whiteboards with automatic cleaning systems worldwide, an efficient device is expected to completely replace conventional cleaning methods. This study presents the development of a low-cost and effective automatic whiteboard cleaning system using locally sourced materials. Various design factors were considered to surpass the capabilities of existing systems. Our testing and results demonstrate that our design can clean specific areas of the whiteboard, unlike other automatic whiteboard cleaners that clean the entire board at once. The cleaning time recorded was under 19 seconds, and our device leaves zero residue with just a few sweeps of the board. In conclusion, our whiteboard cleaning system is highly efficient and meets market standards with ease

Colony forming units counting is a conventional process carry out in bacteriological laboratories, and it is used to follow the behavior of bacteria in different conditions. Currently exist different systems, automatic or semi-automatic, to counting colony forming units exits, but, in general, many laboratories continue using manual counting, which consumes considerable time and effort from researchers and laboratory employees. This paper presents a mathematical model carry out to segment the colony forming units and, in this way, counting them from a digital image of the sample. The method uses the color space information of some points in the image and shows good behavior for images with many or few colony forming units in the sample, according to manual counting. The results show efficiencies close to 98% with MacConkey agar.

Automatic milking systems (AMSs) are among the earliest Precision Livestock Farming developments that have transformed dairy farming worldwide. This review aims to gather, evaluate, and summarize papers that focus on the use of modeling approaches in the context of AMS. We provided a review of 60 articles with a specific focus on cows’ health, production, and behavior/management. The most used modeling approach was Machine Learning (ML, present in 63% of the studies), followed by statistical analysis (14%), fuzzy algorithms (9%), deterministic mod-els (7%), and detection algorithms (7%). Most of the reviewed studies (82%) focused on the detection of cows' health, specifically mastitis, while only 11% were concerned with milk production. Accurate forecasting of dairy cow milk yield and knowledge on the deviation between expected and observed milk yields of individual cows would be beneficial in dairy cow management. Likewise, the study of cows’ behavior and the herd management in AMSs is under-explored (7%). Despite the increasing use of ML techniques in this field there is still a lack of a robust methodology for their application. In particular, we identified a significant gap in the systematic balancing of positive and negative classes for health prediction models.

In this paper we develop approaches to automatic speech recognition (ASR) development that suit the needs and functions of under-heard language speakers. Our novel contribution to HCI is to show how community-engagement can surface key technical and social issues and opportunities for more effective speech-based systems. We introduce a bespoke toolkit of technologies and showcase how we utilised the toolkit to engage communities of under-heard language speakers; and, through that engagement process, situate key aspects of ASR development in community contexts. The toolkit consists of (1) an information appliance to facilitate spoken-data collection on topics of community interest, (2) a mobile app to create crowd sourced transcripts of collected data, and (3) demonstrator systems to showcase ASR capabilities and to feed back research results to community members. Drawing on the sensibilities we cultivated through this research, we present a series of challenges to the orthodoxy of state-of-the-art approaches to ASR development

Numerous virtual environments with automatic program evaluation have emerged to assist the teaching-learning process, allowing timely feedback. In a review of these environments, we find few studies that focus on an approach centered on refactoring: where students are strongly encouraged to refactor, improving the submitted code to also meet quality criteria. In a traditional environment, the student submits the answer and if it is dynamically correct, he goes to the next question. In this work, we propose a complementary approach based on software engineering metrics, which allow a finer evaluation of the code where the programmer, after having his dynamically correct answer, is invited and encouraged to refactor his solution towards an optimal code that also meets the software quality metrics. The work is based on source code in the Python language and shows which software quality metrics can be used with the purpose of encouraging students to refactor their code in programming fundamentals disciplines.

Automatic fault detection (AFD) is a key technology to optimize the Operation and Maintenance of photovoltaic (PV) systems portfolios. A very common approach to detect faults in PV systems is based on the comparison between measured and simulated performance. Although this approach has been explored by many authors, due to the lack a common basis for evaluating their performance, it is still unclear what are the influencing aspects in the design of AFD algorithms. In this study, a series of AFD algorithms have been tested under real operating conditions, using monitoring data collected over 58 months on 80 rooftop-type PV systems installed in Germany. The results shown that this type of AFD algorithm have the potential to detect up to 82.8% of the energy losses with specificity above 90%. In general, the higher the simulation accuracy, the higher the specificity. The use of less accurate simulations can increase sensitivity at the cost of decreasing specificity. Analyzing the measurements individually makes the algorithm less sensitive to the simulation accuracy. The use of machine learning clustering algorithm for the statistical analysis showed exceptional ability to prevent false alerts, even in cases where the modeling accuracy is not high. If a slightly higher level of false alerts can be tolerated, the analysis of daily PR using a Shewhart chart provides the high sensitivity with an exceptionally simple solution with no need for more complex algorithms for modeling or clustering., 33 pages, 30 figures, preprint submitted to Elsevier Solar Energy

This paper presents a simple control method for the fully automatic parking of an unmanned vehicle. This method is based on the switching control algorithm and backstepping theory. The fully automatic parking is hard to accomplish since it cannot converge to a specified norm, guarantee the convergence rate, and large uncertainties. Global exponential convergence to any prescribed norm bound (e-convergence) is guaranteed, and the convergence rate is explicitly given. For the design of steering laws, a method based on backstepping is proposed and analyzed in detail. The backstepping-based design of a multichain system is obtained. Moreover, an exponentially e-convergent control algorithm is adopted to guarantee both converge norm and convergence rate. Real road experiment results are presented and the results show the effectiveness of the control strategies.

The scientific research process generally starts with the examination of the state of the art, which may involve a vast number of publications. Automatically summarizing scientific articles would help researchers in their investigation by speeding up the research process. The automatic summarization of scientific articles differs from the summarization of generic texts due to their specific structure and inclusion of citation sentences. Most of the valuable information in scientific articles is presented in tables, figures, and algorithm pseudocode. These elements, however, do not usually appear in a generic text. Therefore, several approaches that consider the particularity of a scientific article structure were proposed to enhance the quality of the generated summary, resulting in ad hoc automatic summarizers. This paper provides a comprehensive study of the state of the art in this field and discusses some future research directions. It particularly presents a review of approaches developed during the last decade, the corpora used, and their evaluation methods. It also discusses their limitations and points out some open problems. The conclusions of this study highlight the prevalence of extractive techniques for the automatic summarization of single monolingual articles using a combination of statistical, natural language processing, and machine learning techniques. The absence of benchmark corpora and gold standard summaries for scientific articles remains the main issue for this task.

In this paper, we focus on developing automatic assessment (AA) for a topic that has some difficulties in its practical assessment: object oriented programming (OOP). For evaluating that the OOP principles have been correctly applied to a real application, we use unit testing. In this paper, we focus on prioritizing that the students understand and apply correctly complex OOP principles and that they design properly the classes (including their relationships). In addition, we focus on the Python programming language rather than the typical previous works' focus in this area. Thus, we present a real case study of a practical assignment, in which the students have to implement characters for a video game. This assignment has the particularities and advantages that it is incremental and that it applies all four OOP principles within a single assignment. We also present its solution with the UML class diagram description. Furthermore, we provide unit testing for this case study and give general advice for generalizing the unit tests to other real case scenarios. Finally, we corroborate the effectiveness of our approach with positive student evaluations

The automatic modulation classification (AMC) plays an important and necessary role in the truncated wireless signal, which is used in modern communications. The proposed convolution neural network (CNN) for AMC is based on a method of feature expansion by integrating I/Q (time form) with r/Ɵ (polar form) in order to take advantage of two things: first, feature expansion helps to increase features; the second is that converting to polar form helps to increase classification accuracy for higher order modulation due to diversity in polar form. CNN consists of six blocks. Each block contains symmetric and asymmetric filters, as well as max and average pooling filters. This paper uses DeepSig: RadioML which is a dataset of 24 modulation classes. The proposed network has outperformed many recent papers in terms of classification accuracy for 24 modulation types, with a classification accuracy of up to 96.06 at an SNR=20 dB.

The detection of objects on the seafloor is a complex task. The domain of the detection and classification of naval mines is additionally complicated by the high risk nature of the task. Autonomous underwater vehicles (AUVs) have been used in naval mine countermeasures (MCM) operations to search large areas using sensors such as sidescan or synthetic aperture sonars. These sensors generally have a high coverage rate, while sacrificing spatial resolution. Conversely, sensors with higher resolution but lower coverage (such as forward-looking sonars and electro-optical cameras) are employed for the later classification and identification stages of the MCM mission. However, to autonomously execute a target reacquisition mission, it is important to be able to collect and process data automatically and, in near real time, onboard an AUV. For this purpose, an automatic target recognition (ATR) system is required. This article proposes an ATR, which can be used onboard an autonomous vehicle, capable of detecting mine-like objects in forward-looking sonar data. The ATR combines a detector and a classifier, based on convolutional neural network models, with a probabilistic grid map that filters out false positives and combines reported detections at nearby locations. A strategy, combining a survey pattern with target-mapping maneuvers automatically activated by the ATR, has been designed to maximize the performance of this ATR. The whole system has been tested in simulation as well as using data from previous MCM exercises, the results of which are presented here This document is the results of the research project funded by the EU project Open Cooperation for European mAritime awareNess (OCEAN2020)

Tensor rank learning for canonical polyadic decomposition (CPD) has long been deemed as an essential yet challenging problem. In particular, since the tensor rank controls the complexity of the CPD model, its inaccurate learning would cause overfitting to noise or underfitting to the signal sources, and even destroy the interpretability of model parameters. However, the optimal determination of a tensor rank is known to be a non-deterministic polynomial-time hard (NP-hard) task. Rather than exhaustively searching for the best tensor rank via trial-and-error experiments, Bayesian inference under the Gaussian-gamma prior was introduced in the context of probabilistic CPD modeling, and it was shown to be an effective strategy for automatic tensor rank determination. This triggered flourishing research on other structured tensor CPDs with automatic tensor rank learning. On the other side of the coin, these research works also reveal that the Gaussian-gamma model does not perform well for high-rank tensors and/or low signal-to-noise ratios (SNRs). To overcome these drawbacks, in this paper, we introduce a more advanced generalized hyperbolic (GH) prior to the probabilistic CPD model, which not only includes the Gaussian-gamma model as a special case, but also is more flexible to adapt to different levels of sparsity. Based on this novel probabilistic model, an algorithm is developed under the framework of variational inference, where each update is obtained in a closed-form. Extensive numerical results, using synthetic data and real-world datasets, demonstrate the significantly improved performance of the proposed method in learning both low as well as high tensor ranks even for low SNR cases.

International audience; We present a new method to automatically identify the different arteries present in an abdominal aortic segmentation. In this approach, the arterial system is first represented by a vascular tree, extracted from the segmentation and containing the topologic and geometric features (branch position, branch direction, branch length, branch diameter) of the arterial system. Then, the branches of the vascular tree are matched with the main arteries origi- nating from the aorta: celiac artery, superior mesenteric artery, renal arteries and common iliac arteries. This match is determined by maximizing a similarity measure between the dif- ferent branches and corresponding arteries. We evaluate this method on 239 segmentations obtained from 102 different patients. The results demonstrate the accuracy of the proposed method, capable of delivering an error of less than 2.5% for the identification of the celiac and superior mesenteric arteries, 8.4% for the renal arteries, and 2.1% for the common iliac arteries.

Pusat pengatur beban menggunakan Automatic Generation Controller (AGC) sebagai regulasi frekuensi real time untuk memastikan keseimbangan antara permintaan beban dengan ketersediaan pembangkitan. Ketidakpastian variasi beban, dan adanya keterbatasan respon pembangkit (GRC) mempengaruhi respon dinamik frekuensi sistem dalam periode menit. Sistem Jawa Bali menerapkan AGC menggunakan metode integrator dengan 51,18 % partisipasi aktif pembangkit. Penelitian ini mengusulkan AGC menggunakan metode PID controller dengan mempertimbangkan variasi karakteristik beban dan keterbatasan ramp rate up/down pembangkit. Pemodelan AGC menggunakan PID controller memberikan respon pemulihan lebih cepat 2,3 detik dibandingkan metode eksisting dan mengelola kenaikan pembebanan lebih besar pada pembangki dengan biaya lebih rendah. Pembatasan ramp rate up/down pembangkit mengakibatkan perbedaan waktu dalam respon pemulihan dan berpengaruh pada fluktuasi frekuensi sistem, sedangkan variasi karakteristik beban tidak banyak mempengaruhi respon dinamik frekuensi sistem. Metode yang diusulkan disimulasikan dengan menggunakan Matlab Simulink dengan pemodelan untuk 2 pembangkit pada kontrol area tunggal dengan variasi karakteristik beban dan pembatasan pembangkit menggunakan data-data Sistem Jawa Bali

This paper presents a new metaheuristic algorithm by enhancing one of the recently proposed optimizers named Aquila optimizer (AO). The enhanced AO (enAO) algorithm is constructed by employing a novel modified opposition-based learning (OBL) mechanism and Nelder-Mead (NM) simplex search method. The novel modified OBL aids the AO in further diversification while the NM method increases the intensification. The enAO algorithm is first demonstrated to have more extraordinary ability than the original AO algorithm by employing challenging benchmark functions from the CEC 2019 test suite. The constructed enAO algorithm is proposed to design a PID plus second-order derivative (PIDD2) controller used in an automatic voltage regulator (AVR) system. To reach better efficiency, a novel objective function is also proposed in this paper. Initially, the proposed enAO-PIDD2 approach is demonstrated to be superior in terms of transient and frequency responses along with robustness and disturbance rejection compared to other available and best performing PID, fractional order PID (FOPID), PID acceleration (PIDA), and PIDD2 controllers tuned with different practical algorithms. Moreover, the superior performance of the proposed approach is also demonstrated comparatively using other available techniques for the AVR system reported in the last six years.

The day-to-day growth of online information necessitates intensive research in automatic text summarization (ATS). The ATS software produces summary text by extracting important information from the original text. With the help of summaries, users can easily read and understand the documents of interest. Most of the approaches for ATS used only local properties of text. Moreover, the numerous properties make the sentence selection difficult and complicated. So this article uses a graph based summarization to utilize structural and global properties of text. It introduces maximal clique based sentence selection (MCBSS) algorithm to select important and non-redundant sentences that cover all concepts of the input text for summary. The MCBSS algorithm finds novel information using maximal cliques (MCs). The experimental results of recall oriented understudy for gisting evaluation (ROUGE) on Timeline dataset show that the proposed work outperforms the existing graph algorithms Bushy Path (BP), Aggregate Similarity (AS), and TextRank (TR).

Short answer question is one of the methods used to evaluate student cognitive abilities, including memorizing, designing, and freely expressing answers based on their thoughts. Unfortunately, grading short answers is more complicated than grading multiple choices answers. For that problem, several studies have tried to build an artificial intelligence system called automatic short answer grading (ASAG). We tried to improve the accuracy of the ASAG system at scoring student answers in Indonesian by enhancing the earlier state-of-the-art models and methods. They were the bidirectional encoder representations from transformer (BERT) with fine-tuning approach and ridge regression models utilizing advanced feature extraction. We conducted this study by doing stages of literature review, data set preparation, model development, implementation, and comparison. Using two different ASAG data sets, the best result of this study was an achievement of 0.9508 in pearson’s correlation and 0.4138 in root-mean-square error (RMSE) by the BERT-based model with the fine-tuning approach. This result outperformed the results of the previous studies using the same evaluation metrics. Thus, it proved our ASAG system using the BERT model with fine-tuning approach can improve the accuracy of grading short answers.

Automatic landing of aircraft is a challenging guidance and control task that requires multiple feedback loops and a precise sequence of actions. Increasing operational flexibility and reducing the...

With advanced AI, while every industry is growing at rocket speed, the smart home industry has not reached the next-generation level. A home can only be called a real smart home when it’s completely smart. When it can understand residents' needs and fulfill them in a timely manner. One of the important tasks of maintaining a home is timely groceries tracking and supply maintenance. Grocery tracking models are very famous in the retail industry. But they cannot be applied to home fridge groceries detection as the home fridge/ groceries closet environment is very complicated compared to retail shelving data. In this paper, home grocery tracking problem is resolved by combining retail shelving data with real-time 360 view data points collected data of home groceries storage. By integrating this vision-based object detection system with the supply chain system, grocery tracking, and supply maintenance become completely automated.

Purpose: Automated treatment planning strategies are being widely implemented in clinical routines to reduce inter-planner variability, speed up the optimization process, and improve plan quality. This study aims to evaluate the feasibility and quality of intensity-modulated proton therapy (IMPT) plans generated with four different knowledge-based planning (KBP) pipelines fully integrated into a commercial treatment planning system (TPS). Materials/Methods: A data set containing 60 oropharyngeal cancer patients was split into 11 folds, each containing 47 patients for training, five patients for validation, and five patients for testing. A dose prediction model was trained on each of the folds, resulting in a total of 11 models. Three patients were left out in order to assess if the differences introduced between models were significant. From voxel-based dose predictions, we analyze the two steps that follow the dose prediction: post-processing of the predicted dose and dose mimicking (DM). We focused on the effect of post-processing (PP) or no post-processing (NPP) combined with two different DM algorithms for optimization: the one available in the commercial TPS RayStation (RSM) and a simpler isodose-based mimicking (IBM). Using 55 test patients (five test patients for each model), we evaluated the quality and robustness of the plans generated by the four proposed KBP pipelines (PP-RSM, PP-IBM, NPP-RSM, NPP-IBM). After robust evaluation, dose-volume histogram (DVH) metrics in nominal and worst-case scenarios were compared to those of the manually generated plans. Results: Nominal doses from the four KBP pipelines showed promising results achieving comparable target coverage and improved dose to organs at risk (OARs) compared to the manual plans. However, too optimistic post-processing applied to the dose prediction (i.e. important decrease of the dose to the organs) compromised the robustness of the plans. Even though RSM seemed to partially compensate for the lack of robustness in the PP plans, still 65% of the patients did not achieve the expected robustness levels. NPP-RSM plans seemed to achieve the best trade-off between robustness and OAR sparing. Discussion/Conclusions: PP and DM strategies are crucial steps to generate acceptable robust and deliverable IMPT plans from ML-predicted doses. Before the clinical implementation of any KBP pipeline, the PP and DM parameters predefined by the commercial TPS need to be modified accordingly with a comprehensive feedback loop in which the robustness of the final dose calculations is evaluated. With the right choice of PP and DM parameters, KBP strategies have the potential to generate IMPT plans within clinically acceptable levels comparable to plans manually generated by dosimetrists. © 2023 American Association of Physicists in Medicine.

With advanced AI, every industry is growing at rocket speed, while the smart home industry has not reached the next-generation level. A home can only be called a real smart home, when it’s really and completely smart and understand what the residents want and provide them in a timely manner. The residents should live in the house as if they are leaving in a motel while the house itself takes care of itself and give extra benefits to residents like dress suggestions based on events, weather, and taste of the resident etc. In our research, we are trying to achieve a real smart home by integrating the existing AI home parts with automating the parts of the house which are semi-automated or not yet automated like Closest automation. Through this process, every part of the housework independently and as a whole house system integrates all the single parts of the house. Our smart closet application which has both hardware and a Machine Learning system creates suggested clothing for the residents and makes it ready for them at their dressing table. This process involves zero manual effort either if it’s searching for a particular item or thinking about the perfect match for the day or for a particular top. While our machine learning model is developed based on functional APIs, the hardware system is based on controllers and bar codes logic.

Current pulses are convenient to be actively implemented by a Battery Management System (BMS). However, the Short-Term Features (STF) from currentpulses originate from various sensors with uneven qualities, which hinders one powerful and strong learner with STF for the battery SOH estimation. This paper thus proposes an optimized weak learner formulation procedure for Lithium-ion (Li-ion) battery SOH estimation, which further enables the automatic initialization and integration of the weak learners with STF into an efficient SOH estimation framework. A Pareto Front-based Selection Strategy (PFSS) is designed to select the representative solutions from the non-dominated solutions fed by a Knee point driven Evolutionary Algorithm (KnEA), which guarantees both the diversity and accuracy of the weak learners. Afterwards, the weak learners, whose coefficients are obtained by Selfadaptive Differential Evolution (SaDE), are integrated by a weight-based structure. The proposed method utilizes the weak learners with STF to boost the overall performance of SOH estimation. The validation of the proposed method is proved by LiFePO4/C batteries under accelerated cycling ageing test including one mission profile providing Primary Frequency Regulation (PFR) service to the grid and one constant current profile.

PURPOSE. To develop a variant of the algorithm for the automatic input of the reserve, which consists in transferring the load in case of emergency situations, to make a simulation model in the MatLab® environment corresponding to the developed generalized electrical scheme of the system.METHODS. When solving the problem, the method of digital modeling was used, which consists in the maximum approximation of the system under study to a real object, implemented by means of MatLab.RESULTS. It is proposed to study the methods of synthesis of digital models of compensation of voltage drops by the example of a study of an automatic reserve transfer system, demonstrating an approach to modeling this system. When developing models in the MatLab environment, the parameters of real technical elements and devices and their digital analogues are taken into account. The issue of creating a digital model of an electric drive system, including a model of an asynchronous motor with a short-circuited rotor, is considered. The result, after final refinement, can be used to design a real system in production conditions.CONCLUSIONS. The developed model of the automatic transfer switch system is operable, the time indicators are satisfactory for systems that do not make excessive demands on performances and time intervals. For systems that are sensitive to current inrushes during load transfer, some improvements are required, which are reduced to the implementation of a high-speed automatic switch system. The development of this system is currently at the research stage, namely, the compilation of a load transfer logic that takes into account the phase matching of electrical circuits.