Real Target Research Articles

Fuzzy Type-II Resource Allocation and Target Setting in Data Envelopment Analysis: A Real Case of Gas Refineries

Data envelopment analysis (DEA) is a linear programming method to measure the relative efficiency of a set of decision-making units (DMUs) with multiple inputs and outputs. Some DEA models are used to allocate resources and to set targets. Previous studies in resource allocation and target setting have used deterministic data; however, in real problems resources and targets are not deterministic. In this paper, a type-II fuzzy DEA approach is developed for resource allocation and target setting. A numerical example and a real case study including 11 gas refineries with two inputs and three outputs are provided to illustrate the performance of the proposed approach. The results demonstrate that the proposed method efficiently allocates resources and sets targets in presence of uncertainty. Sensitivity analysis is accomplished based on the variation of the value of targets and resources and the variation of the footprint of uncertainty (FOU) of resources and targets.

Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines

In this paper, a method to build models to monitor and evaluate the health status of wind turbines using Single-hidden Layer Feedforward Neural networks (SLFN) is presented. The models are trained using the Extreme Learning Machines (ELM) strategy. The data used is obtained from the SCADA systems, easily available in modern wind turbines. The ELM technique requires very low computational costs for the training of the models, and thus allows for the integration of a grid-search approach with parallelized instances to find out the optimal model parameters. These models can be built both individually, considering the turbines separately, or as an aggregate for the whole wind plant. The followed strategy consists in predicting a target variable using the rest of the variables of the system/subsystem, computing the error deviation from the real target variable and finally comparing high error values with a selection of alarm events for that system, therefore validating the performance of the model. The experimental results indicate that this methodology leads to the detection of mismatches in the stages of the system’s failure, thus making it possible to schedule the maintenance operation before a critical failure occurs. The simplicity of the ELM systems and the ease with which the parameters can be adjusted make it a realistic option to be implemented in wind turbine models to work in real time.

Put yourself in my wheelchair: Perspective‐taking can reduce prejudice toward people with disabilities and other stigmatized groups

AbstractThis study aimed to compare perspective‐taking with a hypothetical target and perspective‐taking occurring during a real interaction with an outgroup member in reducing prejudice toward people with disabilities and other groups (e.g., immigrants, homosexual people), via increased empathy. We adopted an experimental design with two treatment groups (Hypothetical target vs. Real target) and one control group (no intervention), one pretest and two posttest measures. Participants, who were 437 students aged 11–17 years (M = 14.28; SD = 1.17), were randomly assigned to the various conditions on a classroom basis. Perspective‐taking was facilitated asking participants to travel in a wheelchair on a path that simulated a real pavement in the presence (or not) of an individual with a motor disability. Findings showed that perspective‐taking in the presence of a real target was more effective in reducing prejudice toward people with disabilities than perspective‐taking task without encountering an outgroup member. Perspective‐taking with a hypothetical target was not sufficient to increase participants' empathy toward people with disabilities, which instead was enhanced when the task was performed at the presence of a real member of that group. Positive effects produced by perspective‐taking (with both a hypothetical and a real target) were transferred to immigrants and homosexuals. These findings suggest that, when possible, perspective‐taking in the presence of the target, which can improve empathy by favoring a two‐way exchange process, is highly recommended to improve attitudes toward different stigmatized groups, not necessarily targeted by the intervention.

Who's ‘Taking’ From Whom? The Supreme Court's Real Target - Farmworkers' Organising Rights

10 | International Union Rights | 28/2 FOCUS | A CHANGING POST-PANDEMIC WORLD FOR LABOUR Who’s ‘Taking’ From Whom? The Supreme Court’s Real Target - Farmworkers’ Organising Rights Most of the media coverage of the recent Supreme Court decision about the farmworker access rule took for granted the way growers, and the court, defined this regulation. Jess Bravin in the Wall Street Journal called it ‘a regulation giving union organisers the right to visit farmworkers’. The first line of the rightwing majority’s opinion1 called it ‘A California regulation [which] grants labour organisations a ‘right to take access’ to an agricultural employer’s property’. The court, and the growers, deliberately confuse the mechanism of the rule with rights, calling it a right of organisers or organisations. It is not. The right the rule implements is simple. When workers are protesting and organising a union in the fields, they have a right to talk to union representatives at work. It’s a right of workers, rather than a right of union representatives. Rolling back this right, and the ability of farmworkers to organise against their endemic poverty, is the main target of the Supreme Court’s attack. At Cedar Point Nursery, the grower that filed the case heard by the court, the stakes were clear. Cedar Point is a nursery growing rootstock for commercial strawberry growers in Dorris, a remote town in northern California near the Oregon border. Hundreds of workers migrate here from their homes in central and southern California every year to harvest, trim and pack the plants. In 2015 Cedar Point labourers walked out to protest conditions that included, according to worker Jessica Rodriguez2, low wages, dirty bathrooms, and harassment from supervisors. They called the United Farm Workers, which sent organisers and implemented the access rule to talk with them on the property. The strike lasted for just a day, and after the strikers returned to their jobs, the organising effort fizzled out. No election was ever held to begin the process of trying to get a contract. What happened at Cedar Point is not unusual. The following spring in McFarland, in the densely farmed San Joaquin Valley, hundreds of workers struck the blueberry fields of Gourmet Trading over similar issues. Support for the organising was overwhelming. They called the UFW after they’d struck. Once they returned to work the union filed for access, and workers held meetings after work at the ranch. They voted for the union a few days later, and today they work under a union contract. In 1996, during a huge campaign to organise the strawberry industry in Watsonville3, UFW organisers visited picking crews in dozens of fields. They taped butcher paper on the walls of the portapotties during lunchtime meetings. Strawberry workers wrote down their demands for raising some of the lowest wages in agriculture, and planned marches to the company offices to announce them. In all these cases the access rule provided a way for workers to understand the organising process and get help with it. Farmworkers need this because of the nature of the work. They are often migrants, working in a harvest in one area of California although they live in another. Cedar Point’s workers lived hundreds of miles from Dorris, and during the work season slept in motel rooms and temporary housing. At Gourmet Trading some pickers travelled an hour or more to get to the field every day. Those distances make it hard—and sometimes impossible— for people to meet with union organisers at home. According to the Handbook4 of the Agricultural Labor Relations Board, which administers California’s Agricultural Labor Relations Act, ‘The access regulations ... are meant to insure that farm workers, who often may be contacted only at their work place, have an opportunity to be informed with minimal interruption of working activities’. Organising a union is a collective process. Workers need to talk with each other about it. When the Pacific Legal Foundation argued the Cedar Point case in 20175 before the Ninth District of the U.S. Court of Appeals, and lost, its attorney Wen Fa asserted, ‘All the workers live in houses or hotels. Many have cellphones’. Even if this were...

Who's ‘Taking’ From Whom? The Supreme Court's Real Target - Farmworkers' Organising Rights

Who's ‘Taking’ From Whom? The Supreme Court's Real Target - Farmworkers' Organising Rights

Target Detection in Clutter/Interference Regions Based on Deep Feature Fusion for HFSWR

High-frequency surface wave radar (HFSWR) is of great significance for maritime detection, but in the HFSWR echo signal, ship targets are often submerged in a variety of clutter and interference, making it difficult to detect vessels. In this paper, we propose an intelligent detection algorithm for targets concealed in strong clutter and complex interference environments. The algorithm has two stages: preprocessing and target detection. In the preprocessing stage, faster region-based convolutional neural networks Faster R-CNN are designed to identify and locate clutter and interference regions in the range Doppler spectrum; in the target detection stage, a two-level cascade algorithm is proposed. First, an extremum detection algorithm is proposed to identify suspicious target points in the clutter/interference regions, including real and false target points, to quickly obtain potential target positions. Second, in consideration of the characteristics of radar targets, two lightweight networks are designed to extract the CNN features and the stacked autoencoder features of the potential target locations. Then, fusion features are obtained and sent to an extreme learning machine that acts as a second-level classifier to distinguish between real and false target points. Experiments show that the proposed HFSWR target-detection algorithm has better performance for vessel detection in clutter/interference regions than the current mainstream detection algorithms.

Toward a Just Food System

Toward a Just Food System Gabriel N. Rosenberg (bio) and Jan Dutkiewicz (bio) Animal, Vegetable, Junk: A History of Food, from Sustainable to Suicidal by Mark Bittman Houghton Mifflin Harcourt, 2021, 384 pp. "Get big or get out." It's a statement that lives in infamy in the history of American agriculture. Ascribed to Richard [End Page 197] Nixon's secretary of agriculture, Earl Butz, it marked the moment when farm policy took a catastrophic turn by throwing family farms to the agribusiness wolves. It's an idea so callous that it's been seared into the minds of generations of aggrieved small farmers, food writers, and activists and repeated in scores of food and farming tracts, including Animal, Vegetable, Junk, the new book by the prolific cookbook author and former New York Times food columnist Mark Bittman. But there is scant evidence that Butz actually said it. Some of our finest food writers, including Michael Pollan, Dan Barber, Ted Genoways, and now Bittman, all attribute the claim to Butz. But others, including Wendell Berry, Tom Philpott, and Catherine Friend, pin the blame on Ezra Taft Benson, the chief of the U.S. Department of Agriculture (USDA) under Dwight Eisenhower, which would mean the fatal dysfunction of the nation's farm policy started at least a decade earlier. But Benson probably didn't say it either. We could find no firsthand, contemporaneous accounts of either Benson or Butz saying these words and were unable to locate any newspaper articles reporting it. While so many authors use the quotation, they cite only each other or nothing at all, and not one is able to give a date and location. The appeal of this rural legend is that it feels correct. It fits common complaints about the American food system and implies a clear solution: hitting reverse on a half-century of failed food policies by going small and local. With his latest book, Mark Bittman wades into the debate about our troubled food system and falls back on this canonical story. Like the authors mentioned above, Bittman argues that farmers producing "real food" have been undone by corporate forces and their government enablers, leaving rural America damaged, the environment despoiled, and consumers sick from gobbling down ultra-processed "junk." But Animal, Vegetable, Junk is also a story of how food and farming have always been remade and reinvented. It's an expansive, ambitious book propelled by Bittman's claim that "Food not only affects everything, it represents everything." The book develops over three acts. The first section is a grand historical tour of humanity's relationship to food. Starting with our earliest nomadic ancestors foraging for food before developing tools, domesticating animals, and eventually establishing settled agriculture, it sweeps across continents and millennia. In a style that will be familiar to readers of Yuval Noah Harari or Jared Diamond, Bittman's panoramic history shows how food and farming have been central to the rise and fall of entire empires and civilizations, as well as the trajectory of their laws, customs, hierarchies, systems of labor, and technological and scientific innovations. Bittman writes about the accomplishments and failures of the massive agricultural and irrigation projects of vast ancient civilizations, often built using corvée labor and despotic rule, in mostly descriptive rather than judgmental language. This history documents the myriad forms, from miniscule to impressively large, that agriculture has taken. It's only when Bittman turns to colonial British and then American agriculture that his real target becomes clear: the current capitalist food system and its grotesque scale, labor exploitation, and harmful consequences to human, animal, and planetary health. This is the focus of the second—and strongest—part of the book. It was capitalism that drove the colonization of what is now the United States, and Bittman traces how a settler-colonial society displaced thriving indigenous communities and their food cultures and replaced them with cash crops dependent on slave labor and small-scale farms and ranches. Much of this was the direct result of first colonial and then federal policy, perhaps most evident in the land grants given to white farmers to produce food and settle land during westward expansion. Yet...

Intelligent Traffic Light Control System Based On Traffic Environment Using Deep Learning

At present, the traffic control frameworks in India, need insight and go about as an open-loop control framework, with no input or detecting system. Present technologies use Inductive loops and sensors to detect the number of vehicles passing by. It is a very inefficient and expensive way to make traffic lights adaptive. Using a simple CCTV camera can improve the conditions. The visual tracking of objects is amongst the most critical areas of computer vision and deep learning. The objective of this work was to develop the traffic control framework by presenting a detecting system, which gives an input to the current system, with the goal that it can adjust the changing traffic density patterns and provides a vital sign to the controller in a continuous activity. Using this method, improvement of the traffic signal switching expands the street limit, saves time for voyaging, and prevents traffic congestion. The framework additionally goes for consolidating exceptional arrangements for clearing the path for emergency vehicles. In this paper, we will detect and track vehicles on a video stream and count those going through a defined line and to ultimately give an idea of what the real-time on-street situation is across the road network. Our real target is to advance the deferral in the travel of vehicles in odd hours of the day. It uses the YOLO (“You Only Look Once”) object detection technique to detect objects on each of the video frames And SORT (Simple Online and Real-time Tracking algorithm) to track those objects over different frames. Once the objects are detected and tracked over different frames, a simple mathematical calculation is applied to count the intersections between the vehicle’s previous and current frame positions with a defined line.However, accuracy drops when vehicles are either close together or have large shadows, dark vehicles do not always meet the detection criteria, and night scenes are challenging to resolve as headlight beams can create large areas that meet threshold criteria. We are focusing on the Indian roads where the traffic conditions are harsh and abrupt. We have already tested it on a live feed from various traffic prone roads and have found satisfactory results. Other Adaptive Traffic Light Systems were not able to work on the Indian traffic conditions. Our model has the edge over the other by performing at par on Indian roads. We’ll increase or decrease the timer according to the conditions of the roads. This will tremendously improve the traffic conditions at a very low cost. Inductive loops are a feasible but expensive method. So, this system reduces costs and provides quick results.

Infrared Small Maritime Target Detection Based on Integrated Target Saliency Measure

Robust and effective detection of a small target in an infrared maritime image is a key technology of maritime target search and tracking applications. Infrared small target detection is a challenging task due to the factors such as dim small targets and various complex backgrounds caused by sun glitters and strong waves. In this article, the integrated target saliency measure (ITSM) based on local and nonlocal spatial information is proposed to improve target detection performance. We combine local heterogeneity property of targets and nonlocal self-correlation property of background with targets' sparsity to separate real targets from background clutters. First, local heterogeneity calculation based on cross-window standard deviation (CSD) is proposed to extract candidate targets preliminarily, which enhances the local intensity difference between small targets and neighboring background. Meanwhile, low-rank representation (LRR) is applied to background prediction and removal, which is followed by adaptive threshold segmentation to enhance target saliency. Finally, we integrate the results obtained from the two steps mentioned above to further enhance targets and suppress background clutters. Then, real targets are extracted by an iterative threshold on the integrated map so as to generate the seed map, in addition, the target expansion strategy is exploited to keep full target areas. Experimental results on six datasets show that the proposed method outperforms the state-of-the-art methods in terms of robustness and target detection accuracy.

Path planning and task assignment of the multi-AUVs system based on the hybrid bio-inspired SOM algorithm with neural wave structure

A hybrid bio-inspired self-organizing map neural network algorithm is proposed for path planning and task assignment for a multi-autonomous underwater vehicle (AUV) system within a mixed (dynamic and static) three-dimensional (3D) environment. A 3D hybrid bio-inspired neural network model is established to represent the underwater environment and the distribution of the neuron pheromone content gradually diffusing, centered on the source point of the neural wave. Through self-regulation of the neural wave diffusion, the targets can achieve self-adaptive capabilities. “Multiple Newton interpolation” is used to identify the real target among interference targets, and the multi-AUV system transitions from tracking the false target to tracking the real target. Based on the principle of AUV individual kinematics, a velocity vector synthesis algorithm is proposed to overcome the interference of ocean currents. Simulation studies performed in five different environments demonstrate that the proposed algorithm has high adaptability, and the potential for wide application because its neural waves can be updated in real time.

CAREDAS: a Comprehensive Architecture for a Redundant and Evolutive Data Acquisition System for JHR reactor

A new material testing reactor Jules Horowitz Reactor is under construction at CEA Cadarache. The materials to be irradiated will be placed into experimental devices around the reactor. Process and measurements of experimental devices will be carried out by command control. A data acquisition system having processing performances will be associated to the programmable logic controller. The challenge is to design and realize for twenty experiment devices a high availability data acquisition system architecture for 50 years of sustainability. The real time target will achieve 24/7 data acquisition and real time processing. This scalable architecture could be use as well for JHR experimental devices with high availability as for testbed. This architecture could be run on a standalone station for a measuring bench or deployed on cluster for high availability. CAREDAS’s design is modular and use proven widely used open source solutions. All parts are independent from each other and can be replaced with another technology with the same functionalities. This ensures sustainability and control of software sources.

Compressive sensing multi-target diffusive source localisation using sparse recovery algorithms in sensor networks

According to the multi-target diffusive source localisation in sensor networks, a compressive sensing sparse recovery algorithm was proposed for the mismatching problem of the target sources sparsity and the high-dimensional redundant sampling signals. Firstly, the compressive sensing system model and the related terms were given and explained. Then, the joint optimal estimation of the sparse diffusive source vector and the diffusion distribution state were realised with the variational Bayesian expectation maximisation algorithm (VB-EM). In which, the dynamic compressive sensing dictionary model of the real target source sparse representation was designed and adjusted with the grid division parameters optimisation for the dictionary mismatch problem solving. Finally, the simulation results show that the proposed compressive sensing method with VB-EM algorithm could effectively achieve the diffusive source parameters estimation and its diffusion distribution state prediction. Compared with the traditional compressive sensing sparse recovery algorithms, it could obtain higher robustness performance for the rapid and accurate localisation in complex environment.

Compressive sensing multi-target diffusive source localisation using sparse recovery algorithms in sensor networks

According to the multi-target diffusive source localisation in sensor networks, a compressive sensing sparse recovery algorithm was proposed for the mismatching problem of the target sources sparsity and the high-dimensional redundant sampling signals. Firstly, the compressive sensing system model and the related terms were given and explained. Then, the joint optimal estimation of the sparse diffusive source vector and the diffusion distribution state were realised with the variational Bayesian expectation maximisation algorithm (VB-EM). In which, the dynamic compressive sensing dictionary model of the real target source sparse representation was designed and adjusted with the grid division parameters optimisation for the dictionary mismatch problem solving. Finally, the simulation results show that the proposed compressive sensing method with VB-EM algorithm could effectively achieve the diffusive source parameters estimation and its diffusion distribution state prediction. Compared with the traditional compressive sensing sparse recovery algorithms, it could obtain higher robustness performance for the rapid and accurate localisation in complex environment.

Towards Crossfire Distributed Denial of Service Attack Protection Using Intent-Based Moving Target Defense Over Software-Defined Networking

Crossfire is an indirect target area link-flooding Distributed Denial of Service (DDoS) attack determined to affect the neighbors of the real target. Currently, Crossfire DDoS attacks are acquiring impetus because of their indistinguishability and undetectability. SDN (Software Defined Networking) is a progressing technique because of its adaptability and programmability. Moving Target Defense (MTD) is an arising security strategy to counter attacks by progressively changing the attacked plane. IBN (Intent-based Networking) is another promising methodology for providing dynamic network management. IBN-based MTD can provide efficient MTD solutions because of the concentrated control and observing capacities of the intents when translated into rules inside the SDN control plane. In this paper, a framework for the security of Crossfire DDoS attacks is proposed by making use of Intent-based Traffic modifications through the Open Networking Operating System (ONOS) Rest API and Domain Name System (DNS) port redirection. In this paper, we exploited Intent-based MTD to divert traffic from the principal host to virtual shadow hosts to counter this attack. Traffic redirection helps in masquerading the attacker headed for shadow host and consequently getting the erroneous path towards the network and, hence, the Crossfire attack couldn’t be executed as expected. The proposed technique is simulated using Mininet and ONOS SDN controllers. The outcomes showed traffic is successfully redirected at a low computational expense. Therefore, Crossfire DDoS is efficiently mitigated as promising results are found.

A Registration Framework for the Comparison of Video and Optical See-Through Devices in Interactive Augmented Reality

In this paper, we designed a registration framework that can be used to develop augmented reality environments, where all the real (including the users) and virtual elements are co-localized and registered in a common reference frame. The software is provided together with this paper, to contribute to the research community. The developed framework allows us to perform a quantitative assessment of interaction and egocentric perception in Augmented Reality (AR) environments. We assess perception and interaction in the peripersonal space through a 3D blind reaching task in a simple scenario and an interaction task in a kitchen scenario using both video (VST) and optical see-through (OST) head-worn technologies. Moreover, we carry out the same 3D blind reaching task in real condition (without head-mounted display and reaching real targets). This provides a baseline performance with which to compare the two augmented reality technologies. The blind reaching task results show an underestimation of distances with OST devices and smaller estimation errors in frontal spatial positions when the depth does not change. This happens with both OST and VST devices, compared with the real-world baseline. Such errors are compensated in the interaction kitchen scenario task. Thanks to the egocentric viewing geometry and the specific required task, which constrain the position perception on a table, both VST and OST have comparable and effective performance. Thus, our results show that such technologies have issues, though they can be effectively used in specific real tasks. This does not allow us to choose between VST and OST devices. Still, it provides a baseline and a registration framework for further studies and emphasizes the specificity of perception in interactive AR.

Development of a selective three-dimensional HPLC system for enantiomer discriminated analysis of lactate and 3-hydroxybutyrate in human plasma and urine

For the enantiomer discriminated determination of lactate (LA) and 3-hydroxybutyrate (3HB) in various complicated samples, a three-dimensional HPLC (3D-HPLC) system has been designed and developed by investigating the separation of the target analytes from unknown substances observed in the real target matrices. LA and 3HB were pre-column derivatized with 4-nitro-7-piperazino-2,1,3-benzoxadiazole for the sensitive fluorescence detection and introduced into the 3D-HPLC system composed of reversed-phase, mixed-mode and enantioselective separations. The present method was validated by calibration curves, precision and accuracy using standard solutions and human samples, and sufficient values were obtained. Using the method, the levels of d-LA, l-LA, d-3Hb and l-3HB were determined, and their concentrations were 9.9, 1004.2, 79.7 and 2.1 μM in the human plasma and 16.0, 86.6, 8.7 and 4.8 μM in the human urine, respectively. The present 3D-HPLC system could selectively determine trace amounts of the target hydroxy acid enantiomers without disturbance of the intrinsic interfering substances in complicated matrices and the applications to various disease samples are expected.

Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images.

Cell or nucleus detection is a fundamental task in microscopy image analysis and has recently achieved state-of-the-art performance by using deep neural networks. However, training supervised deep models such as convolutional neural networks (CNNs) usually requires sufficient annotated image data, which is prohibitively expensive or unavailable in some applications. Additionally, when applying a CNN to new datasets, it is common to annotate individual cells/nuclei in those target datasets for model re-learning, leading to inefficient and low-throughput image analysis. To tackle these problems, we present a bidirectional, adversarial domain adaptation method for nucleus detection on cross-modality microscopy image data. Specifically, the method learns a deep regression model for individual nucleus detection with both source-to-target and target-to-source image translation. In addition, we explicitly extend this unsupervised domain adaptation method to a semi-supervised learning situation and further boost the nucleus detection performance. We evaluate the proposed method on three cross-modality microscopy image datasets, which cover a wide variety of microscopy imaging protocols or modalities, and obtain a significant improvement in nucleus detection compared to reference baseline approaches. In addition, our semi-supervised method is very competitive with recent fully supervised learning models trained with all real target training labels.

Research on the Application of Binocular Vision-Based Pose Estimation Technology in Live-Line Working

To engage robots in live-line working and reduce the cost of electric power companies, this study proposes a binocular vision-based pose estimation method that can automatically identify the target’s pose, guide the robotic arms to grasp the tools and fulfill live-line working tasks. First, this method matches and calculates the deep information of images via the binocular camera, and converts the images into the cloud of points; then, the advanced image segmentation technology is used to recognize the tools, and 3D modelling is conducted for the objects to accurately match the target image and the real target; the iterative closest point (ICP) method is used to solve the spatial pose of the target corresponding to the robot and thus guide the robots in tasks. Meanwhile, experiments are conducted to simulate the actual work process, and the result shows that the proposed method can accurately estimate the pose of targets and has good robustness and application value.

Marketing research in creating the concept of a restaurant business

Abstract. The essence of one of the methods of marketing research - questionnaires is revealed. Based on a questionnaire and a survey of 1,500 respondents conducted on social networks, the market of modern restaurant services (for example, bars) in Chernivtsi was analyzed, the most popular among existing establishments of this type were identified, respondents' preferences, attitudes to conceptual establishments in general and to the idea of creating a new bar in Chernivtsi in particular. Introduction. When creating any new project, marketing research occupies a significant place, due to the need to reduce the risk of making the wrong decision by better knowledge and understanding of the state and dynamics of environmental factors. The role of this research is to assess the needs, demands and demand of consumers, which helps to create a program to meet them, identify and identify both problems and opportunities for the firm to take a competitive position in the market, reduce risk and uncertainty, increase the likelihood of marketing success. Presentation of the main results of the study. The main purpose of our own marketing research is to obtain information on the largest potential competitors in the restaurant market in Chernivtsi, motives and factors that affect the preferences of potential consumers of restaurant services (especially services provided in bars), which will further help assess the possible prospects own bar in Chernivtsi. For my research, we chose a survey method, namely a questionnaire that was distributed through social networks, which was able to provide quality primary information. It was made with the Google platform, which is fully equipped with all the necessary services for remote surveys. Almost 1,500 respondents took part in the survey. In general, the questionnaire consists of an introductory part and 2 sections. In the first section, firstly you need to enter some demographic data (name, age, gender, city of residence, type of activity) and answer about the general attitude to the bar as a place of rest. First of all, it is necessary to determine the real target audience. The second section contains questions specifically about bars in Chernivtsi. This serves as a foundation for further in-depth study of the Chernivtsi restaurant market. In total, the respondent had to answer 22 questions, 20 of which were mandatory. Among them are 10 open, 5 semi-closed with the option "Other" and 7 closed. The main results of the research after processing all the answers in the questionnaire are following: - Almost all participants are adults (only 4% is under 18) - 69% of women and 31% of men took part in the survey - 73% of all respondents are residents of Chernivtsi and Chernivtsi oblast - The majority of respondents is students, that is the 86% of the total - 39% allow themselves to visit bars several times a month - 54% are ready to leave up to UAH 300 at the bar - 94.5% visit bars to relax with friends - 84% of respondents say that they choose a bar only on the advice of friends - 88% consider that the concept is important in bars - 41% visit only the most famous bars (in Chernivtsi they are “Hoyra”, “VYO”, “Bartka”, “Nashe pyvo”) Besides, it must be noticed that respondents have helped to identify the shortcomings of the bars that most upset visitors, such as unprofessional and rude staff; dirt and smoking in the hall; inadequate clients in a state of intoxication; mismatch of price and quality. Moreover, according to respondents’ thoughts, the ideal bar should include good atmosphere; varied and delicious menu with adequate prices; competent staff; harmonious interior and music. Conclusions. So, after analyzing all the completed questionnaires from respondents, we can conclude that potential clients are students and already working residents of Chernivtsi, aged 18 and older, among whom visiting bars for leisure is a very popular type of free time. The concept of restaurants or bars plays an important role in choosing the institution and its subsequent visit. Most of the respondents note that the concept and harmonious combination of all its components is an integral part of the success and love of customers of existing bars in Chernivtsi and these establishments on the market in general. Therefore, I believe that creating the concept of your own bar is important and quite relevant today, because first of all it is necessary to stand out from other competitors and to capture the majority of potential consumers. And the originality of the idea and the harmonious existence of all other criteria and indicators that are important to customers are the main key for achieving this goal.

Active Sonar Target Classification with Power-Normalized Cepstral Coefficients and Convolutional Neural Network

Detection and classification of unidentified underwater targets maneuvering in complex underwater environments are critical for active sonar systems. In previous studies, many detection methods were applied to separate targets from the clutter using signals that exceed a preset threshold determined by the sonar console operator. This is because the high signal-to-noise ratio target has enough feature vector components to separate. However, in a real environment, the signal-to-noise ratio of the received target does not always exceed the threshold. Therefore, a target detection algorithm for various target signal-to-noise ratio environments is required; strong clutter energy can lead to false detection, while weak target signals reduce the probability of detection. It also uses long pulse repetition intervals for long-range detection and high ambient noise, requiring classification processing for each ping without accumulating pings. In this study, a target classification algorithm is proposed that can be applied to signals in real underwater environments above the noise level without a threshold set by the sonar console operator, and the classification performance of the algorithm is verified. The active sonar for long-range target detection has low-resolution data; thus, feature vector extraction algorithms are required. Feature vectors are extracted from the experimental data using Power-Normalized Cepstral Coefficients for target classification. Feature vectors are also extracted with Mel-Frequency Cepstral Coefficients and compared with the proposed algorithm. A convolutional neural network was employed as the classifier. In addition, the proposed algorithm is to be compared with the result of target classification using a spectrogram and convolutional neural network. Experimental data were obtained using a hull-mounted active sonar system operating on a Korean naval ship in the East Sea of South Korea and a real maneuvering underwater target. From the experimental data with 29 pings, we extracted 361 target and 3351 clutter data. It is difficult to collect real underwater target data from the real sea environment. Therefore, the number of target data was increased using the data augmentation technique. Eighty percent of the data was used for training and the rest was used for testing. Accuracy value curves and classification rate tables are presented for performance analysis and discussion. Results showed that the proposed algorithm has a higher classification rate than Mel-Frequency Cepstral Coefficients without affecting the target classification by the signal level. Additionally, the obtained results showed that target classification is possible within one ping data without any ping accumulation.