Jednoduchý odhad vzdálenosti objektu založený na kamerových záznamech

There has been a quick and effective increase in computer vision research in recent years, and this will continue. Part of this success may be attributed to the adoption and adaptation of Machine Learning methods, while other parts can be attributed to the invention of novel representations and models for specific computer vision challenges, as well as the development of cost-effective solutions. Object detection is one area that has made significant strides in recent years. Object detection has been used in a variety of applications, including robotics, consumer electronics (e.g., smart phones), security, and transportation (e.g., autonomous and assisted driving). In this thesis, the detection task is the first job completed since it enables the acquisition of further information about the identified object as well as about the surrounding scene. Once an instance of an item has been detected, it is possible to gain more information, such as the ability to identify an object and estimated its distance. It is the goal of this study to give a detailed and in-depth explanation of how to find objects and figure out how far apart they are. This thesis is primarily concerned with the creation of object distance measurement and feature extraction algorithms using the You Only Look Once (YOLO) method combined with the Triangle Similarity and Monodepth2 approach for calculating distance with a single fixed camera. The purpose of this thesis is to investigate the detection ability of the method, YOLOv4-tiny, which is one of the most common nowadays. Furthermore, it is more accurate than other detection methods and executes more quickly. The YOLO method outperforms all of the measures we looked at while still delivering a high frame rate for real-time use. Instead of picking the most appealing part of an image, the YOLO technique predicts classes and bounding boxes for the entire image in a single algorithm run. We recommend using a combination of the YOLOv4-tiny and the Triangle Similarity and a very well-known approach called Monodepth2 of the lens camera to estimate the distance between the detected item and the camera. This will allow for a more accurate measurement of the distance. Using the YOLO approach, we detect an object in an image and extract its location and width from the image. This is also known as a virtual image. The items utilized in the tests are photographs of everyday things such as bottles, people, bags, and cars,... By comparing the real and imaginary widths of an object, the triangle similarity approach will be able to determine the focal length of a camera and, as a result, determine the best distance between it and the object. At the end of the process, the linear regression approach is used to forecast the error from the observed distance.V posledních letech došlo k rychlému a efektivnímu nárůstu výzkumu v oblasti počítačového vidění, který bude pokračovat. Část tohoto úspěchu lze přičíst přijetí a přizpůsobení metod strojového učení, zatímco další část lze přičíst vynálezu nových reprezentací a modelů pro specifické problémy počítačového vidění a také vývoji nákladově efektivních řešení. Detekce objektů je jednou z oblastí, která v posledních letech dosáhla významného pokroku. Detekce objektů se používá v řadě aplikací, včetně robotiky, spotřební elektroniky (např. chytrých telefonů), bezpečnosti a dopravy (např. autonomní a asistované řízení). V této práci je detekční úloha první splněnou úlohou, protože umožňuje získat další informace o identifikovaném objektu i o okolní scéně. Jakmile je instance předmětu detekována, je možné získat další informace, například možnost identifikovat objekt a odhadnout jeho vzdálenost. Cílem této studie je podat podrobný a zevrubný výklad o tom, jak najít objekty a zjistit, jak jsou od sebe vzdáleny. Tato práce se zabývá především vytvořením algoritmů pro měření vzdálenosti objektů a extrakci prvků pomocí metody You Only Look Once (YOLO) v kombinaci s přístupem Triangle Similarity a Monodepth2 pro výpočet vzdálenosti pomocí jedné pevné kamery. Cílem této práce je prozkoumat detekční schopnost metody YOLOv4-tiny, která je v současné době jednou z nejrozšířenějších. Navíc je přesnější než ostatní metody detekce a provádí se rychleji. Metoda YOLO překonává všechna námi zkoumaná opatření a zároveň poskytuje vysokou snímkovou frekvenci pro použití v reálném čase. Namísto výběru nejatraktivnější části snímku předpovídá technika YOLO třídy a ohraničující boxy pro celý snímek v jediném běhu algoritmu. Doporučujeme použít kombinaci YOLOv4-tiny a trojúhelníkové podobnosti a velmi známého přístupu nazvaného Monodepth2 objektivu kamery pro odhad vzdálenosti mezi detekovaným předmětem a kamerou. To umožní přesnější měření vzdálenosti. Pomocí přístupu YOLO detekujeme objekt v obraze a extrahujeme z něj jeho polohu a šířku. Tento obraz je také znám jako virtuální obraz. Předměty využité v testech jsou fotografie věcí každodenní potřeby, jako jsou láhve, lidé, tašky a auta,... Porovnáním skutečné a imaginární šířky předmětu bude přístup založený na trojúhelníkové podobnosti schopen určit ohniskovou vzdálenost fotoaparátu a v důsledku toho určit nejlepší vzdálenost mezi ním a předmětem. Na konci procesu se použije přístup lineární regrese k předpovědi chyby ze zjištěné vzdálenosti.460 - Katedra informatikydobř

Lecturer Attitudes and Behavioural Intentions to Use Learning Management Systems in Vietnam

This study aims to explore lecturer attitudes to, and intentions for, using a learning management system (LMS) in a Vietnamese university. Its two main purposes are to (a) identify the factors that influence lecturer attitudes and intentions to use an LMS, and (b) examine the causal relationships among the factors. To achieve this aim, the study used Davis’ (1985) technology acceptance model (TAM) as a baseline. The study expands the original model to include two constructs: perceived internet self-efficacy (PIS), and support to use (SU). The results of the study revealed that PIS was a significant direct predictor of lecturers’ perceived ease of use and behavioural intention to use an LMS. However, the support to use construct did not predict perceived ease of use. The study suggests that institutions should conduct an in-depth survey of teacher needs to assist with making well-informed decisions about developing an LMS for future emergencies

Systematic Risk in Energy Businesses: Empirical Evidence for the ASEAN

This paper is conducted to provide an additional empirical evidence in relation to the estimates of equity beta for energy businesses in the ASEAN-5 including Vietnam, Thailand, the Philippines, Malaysia, and Singapore. Listed energy companies for the period from 2005 to 2015 are used. Quantile regression, together with the OLS and LAD, has been used. Findings from this paper indicate that: (i) as long as the OLS and the LAD approaches are adopted, estimates of equity beta are relatively consistent across various research periods; (ii) estimates of equity beta appear to vary substantial across different quantiles; and (iii) estimates of equity beta have appeared to vary across research periods. However, as an overall level across time and methods, a level of risk faced by a company in the energy sector is below the average of the level of risk for the entire market for the above nations. Keywords: Beta, Listed Energy Firms, Quantile regression, ASEAN JEL Classifications: G11; G1

Equity Beta for Regulated Energy Businesses in Australia: A Revisit

This paper aims to estimate the equity beta – a key input of the Capital Asset Pricing Model, for the energy businesses in Australia in the 11-year period from 2005 to 2015. Various methods are used in this paper including Quantile Regression. Listed companies in the energy industry are considered at individual and portfolio levels. Findings from this paper are  both consistent and contrast with prior related studies: (i) energy sector in Australia face a relatively low risk level compared to the market; (ii) OLS results are higher than LAD; and (iii) QR vary across different percentiles. Keywords: Equity Beta, Quantile regression, Australia. JEL Classifications: G11; G1

Building a Model of Organizational Activities Experience in Natural Sciences under Stem Education Orientation

STEM educational-oriented Natural Science experience activities are learner-oriented activities Taking learners as the center in all natural science research activities applying engineering technology and mathematics STEM in solving practical problems Based on theoretical research on experiential activities STEM education and the characteristics of Natural Sciences the article studies the educational forces inside and outside the school From there it is proposed to build a model of organizational structure for experiential activities in Natural Sciences oriented toward STEM education The results of this research are meaningful in helping principals see the importance and significance of coordinating educational forces inside and outside the school in organizing experiential activities in Natural Sciences according to orientation STEM education for student

Primary Evaluation on Growth Performances of Stress Negative Piétrain Pigs Raised in Hai Phong Province of Vietnam

peer reviewedThe present study was carried out on 19 stress negative Piétrain pigs (Pietrain ReHal), consisting of 13 gilts and 6 young boars imported from Belgium, raised in the livestock farm of Dong Hiep (Hai Phong) in order to evaluate growth performances and their adaptability in the North of Vietnam. Results showed that the average body weight of the whole herd at 2, 4, 5.5, and 8.5 months old was 19.05, 51.05, 85.82, and 119.47 kg, respectively. During the growing periods, except the first stage, the male grew faster than the female and the pigs of the CT genotype grew faster than those of CC genotype although the difference was not significant (P>0.05). The average daily gain (ADG) was 528.56 grams for the whole herd. The ADG was higher for the male (546.48 grams) than for the female (520.29 grams), and its was higher for the CT than the CC, but the difference was not statistically significant (P>0.05). The feed conversion ratio (FCR) was 2.69 kg. The estimated lean percentage at 8.5 months old was 64.08%. The results indicate that Piétrain stress negative pigs could develop well on the farm conditions in Hai Phong, Vietnam

Using Solvent Vapor Annealing for the Enhancement of the Stability and Efficiency of Monolithic Hole-conductor-free Perovskite Solar Cells

In the last few years, perovskite solar cells have attracted enormous interest in the photovoltaic community due to their low cost of materials, tunable band gap, excellent photovoltaic properties and easy process ability at low temperature. In this work, we fabricated hole-conductor-free carbon-based perovskite solar cells with the monolithic structure: glass/FTO/bl-TiO$_{2}$/(mp-TiO$_{2}$/mp-ZrO$_{2}$/mp-carbon) perovskite. The mixed 2D/3D perovskite precursor solution composed of PbI$_{2}$, methylammonium iodide (MAI), and 5-ammoniumvaleric acid iodide (5-AVAI) was drop-casted through triple mesoporous TiO$_{2}$/ZrO$_{2}$/carbon electrode films. We found that the isopropyl alcohol (IPA) solvent vapor annealing strongly influenced on the growth of mixed 2D/3D perovskite on triple mesoscopic layers. It resulted in the better pore filling, better crystalline quality of perovskite layer, thus the improved stability and efficiency of perovskite solar cell was attributed to lower defect concentration and reduced recombination

Spatiotemporal evolution of SARS-CoV-2 Alpha and Delta variants during large nationwide outbreak of COVID-19, Vietnam, 2021

We analyzed 1,303 SARS-CoV-2 whole-genome sequences from Vietnam, and found the Alpha and Delta variants were responsible for a large nationwide outbreak of COVID-19 in 2021. The Delta variant was confined to the AY.57 lineage and caused >1.7 million infections and >32,000 deaths. Viral transmission was strongly affected by nonpharmaceutical interventions

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke