Penilaian Lesan Dada Tidak Bernilai Pada Ttc Menggunakan Morfologi Citra Digital

Sistem Latihan menembak merupakan salah satu hal terpenting dalam dunia militer. Pelaksanaan latihan tersebut terdapat beberapa materi yang diterapkan sesuai dengan kebutuhan TNI AD.. Setiap prajurit TNI AD diwajibkan untuk memperoleh sertifikat tersebut. Salah satu materi latihan menembak dalam TNI AD yaitu Tembak Tempur Cepat. Tembak tempur cepat adalah latihan menembak dengan berjalan yang diperumpamakan bertemu dengan musuh secaa tiba-tiba. Lesan yang digunakan adalah lesan dada tidak bernilai yang bergerak secara tiba-tiba. Dimana sistem pelaksanaannya masih manual yaitu masih menggunakan tenaga manusia baik untuk menggerkan lesan maupun sistem penilaiannya. Sistem penilaian yang masih manual membuat sistem penilaian tidak obyektif. Sistem penilaian masih manual yaitu dengan melihat secara mata visual. Dengan memanfaatkan sinar matahari maka sistem penilaian dapat menggunakan kamera dengan menggunakan metode pengolahan citra digital. Pengolahan citra digital adalah suatu metode yang digunakan untuk membedakan warna. Dengan memanfaatkan sinar matahari maka pengolahan citra sangat cocok digunakan untuk membedakan warna antara lesan yang tidak berlubang dan lesan yang berlubang karena hasil tembakan sehingga nilai tembakan petembak dapat dibaca oleh pengolahan citr

Cognitive changes in conjunctive rule-based category learning: An ERP approach

When learning rule-based categories, sufficient cognitive resources are needed to test hypotheses, maintain the currently active rule in working memory, update rules after feedback, and to select a new rule if necessary. Prior research has demonstrated that conjunctive rules are more complex than unidimensional rules and place greater demands on executive functions like working memory. In our study, event-related potentials (ERPs) were recorded while participants performed a conjunctive rule-based category learning task with trial-by-trial feedback. In line with prior research, correct categorization responses resulted in a larger stimulus-locked late positive complex compared to incorrect responses, possibly indexing the updating of rule information in memory. Incorrect trials elicited a pronounced feedback-locked P300 elicited which suggested a disconnect between perception, and the rule-based strategy. We also examined the differential processing of stimuli that were able to be correctly classified by the suboptimal single-dimensional rule (“easy” stimuli) versus those that could only be correctly classified by the optimal, conjunctive rule (“difficult” stimuli). Among strong learners, a larger, late positive slow wave emerged for difficult compared with easy stimuli, suggesting differential processing of category items even though strong learners performed well on the conjunctive category set. Overall, the findings suggest that ERP combined with computational modelling can be used to better understand the cognitive processes involved in rule-based category learning

Two-level systems: exact solutions and underlying pseudo-supersymmetry

Chains of first-order SUSY transformations for the spin equation are studied in detail. It is shown that the transformation chains are related with a olynomial pseudo-supersymmetry of the system. Simple determinant formulas for the final Hamiltonian of a chain and for solutions of the spin equation are derived. Applications are intended for a two-level atom in an electromagnetic field with a possible time-dependence of the field frequency. For a specific form of this dependence, the time oscillations of the probability to populate the excited level disappear. Under certain conditions this probability becomes a function tending monotonously to a constant value which can exceed 1/2.Comment: to be published in Ann. Phys. (NY), 6 figures, 17 page

The Ramsey method in high-precision mass spectrometry with Penning traps: Experimental results

The highest precision in direct mass measurements is obtained with Penning trap mass spectrometry. Most experiments use the interconversion of the magnetron and cyclotron motional modes of the stored ion due to excitation by external radiofrequency-quadrupole fields. In this work a new excitation scheme, Ramsey's method of time-separated oscillatory fields, has been successfully tested. It has been shown to reduce significantly the uncertainty in the determination of the cyclotron frequency and thus of the ion mass of interest. The theoretical description of the ion motion excited with Ramsey's method in a Penning trap and subsequently the calculation of the resonance line shapes for different excitation times, pulse structures, and detunings of the quadrupole field has been carried out in a quantum mechanical framework and is discussed in detail in the preceding article in this journal by M. Kretzschmar. Here, the new excitation technique has been applied with the ISOLTRAP mass spectrometer at ISOLDE/CERN for mass measurements on stable as well as short-lived nuclides. The experimental resonances are in agreement with the theoretical predictions and a precision gain close to a factor of four was achieved compared to the use of the conventional excitation technique.Comment: 12 pages, 14 figures, 2 table

Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions

Stainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it is essential to develop reliable and accurate design methods that can optimize the material. As novel, reliable soft computation methods, machine learning provided more accurate predictions than analytical formulae and solved highly complex problems. The present study aims to develop machine learning models to predict the cross-section resistance of circular hollow section stainless steel stub column. A parametric study is conducted by varying the diameter, thickness, length, and mechanical properties of the column. This database is used to train, validate, and test machine learning models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), Gene Expression Programming (GEP) and Support Vector Machine Regression (SVMR). Thereafter, results are compared with finite element models and Eurocode 3 (EC3) to assess their accuracy. It was concluded that the EC3 models provided conservative predictions with an average Predicted-to-Actual ratio of 0.698 and Root Mean Square Error (RMSE) of 437.3. The machine learning models presented the highest level of accuracy. However, the SVMR model based on RBF kernel presented a better performance than the ANN, GEP and DTR machine learning models, and RMSE value for SVMR, ANN, GEP and DTR is 22.6, 31.6, 152.84 and 29.07, respectively. The GEP leads to the lowest level of accuracy among the other three machine learning models, yet, it is more accurate than EC3. The machine learning models were implemented in a user-friendly tool, which can be used for design purposes

Presence of the Jehol Biota turtle <i>Ordosemys liaoxiensis</i> in the Early Cretaceous Hengtongshan Formation of southern Jilin Province, China

Recently, a vertebrate assemblage of the Jehol Biota has been reported from the Early Cretaceous Hengtongshan Formation of Xingling Town, Meihekou City, Jilin Province, China. It is dominated by the fishes Lycoptera and Sinamia and the sinemydid turtle Ordosemys. Here, we describe the turtle specimens and referral to Ordosemys liaoxiensis, otherwise known from the older Yixian Formation of the Jehol Biota. It is characterized by a subcircular shell, wide vertebral scales, well-developed plastral fenestrae, and a major contribution from the xiphiplastra to enclose the hypo-xiphiplastral fenestra. As the first Mesozoic turtle of Jilin Province, this record represents the first tetrapod to indicate the presence of the Jehol Biota in the region. Given the geographic and temporal distance from the Yixian Formation, future collections from the Hengtongshan Formation have good potential for evaluating spatiotemporal patterns of the Jehol Biota.</p

Antiretroviral dynamics determines HIV evolution and predicts therapy outcome

Despite the high inhibition of viral replication achieved by current anti-HIV drugs, many patients fail treatment, often with emergence of drug-resistant virus. Clinical observations show that the relationship between adherence and likelihood of resistance differs dramatically across drug class. We developed a mathematical model that explains these observations and makes novel predictions. Our model incorporates drug properties, fitness differences between susceptible and resistant strains, mutation, and adherence. We show that antiviral activity falls quickly for drugs with sharp dose-response curves and short half-lives, such as boosted protease inhibitors, limiting the time when resistance can be selected. We find that poor adherence to such drugs causes failure via growth of susceptible virus, explaining puzzling clinical observations. Furthermore, our model predicts that certain single-pill combination therapies can prevent resistance regardless of patient adherence. Our approach represents a first step for simulating clinical trials and may help select novel drug regimens for investigation.MathematicsPhysic

Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings

The use of periodical elliptically-based web (EBW) openings in high strength steel (HSS) beams has been increasingly popular in recent years mainly because of the high strength-to-weight ratio and the reduction in the floor height as a result of allowing different utility services to pass through the web openings. However, these sections are susceptible to web-post buckling (WPB) failure mode and therefore it is imperative that an accurate design tool is made available for prediction of the web-post buckling capacity. Therefore, the present paper aims to implement the power of various machine learning (ML) methods for prediction of the WPB capacity in HSS beams with (EBW) openings and to assess the performance of existing analytical design model. For this purpose, a numerical model is developed and validated with the aim of conducting a total of 10,764 web-post finite element models, considering S460, S690 and S960 steel grades. This data is employed to train and validate different ML algorithms including Artificial Neural Networks (ANN), Support Vector Machine Regression (SVR) and Gene Expression Programming (GEP). Finally, the paper proposes new design models for WPB resistance prediction. The results are discussed in detail, and they are compared with the numerical models and the existing analytical design method. The proposed design models based on the machine learning predictions are shown to be powerful, reliable and efficient design tools for capacity predictions of the WPB resistance of HSS beams with periodical (EBW) openings