VIGOROUS 3D ANGULAR RESECTION MODEL USING LEVENBERG – MARQUARDT METHOD

The resection in 3D space is a common problem in surveying engineering and photogrammetry based on observed distances, angles, and coordinates. This resection problem is nonlinear and comprises redundant observations which is normally solved using the least-squares method in an iterative approach. In this paper, we introduce a vigorous angular based resection method that converges to the global minimum even with very challenging starting values of the unknowns. The method is based on deriving oblique angles from the measured horizontal and vertical angles by solving spherical triangles. The derived oblique angles tightly connected the rays enclosed between the resection point and the reference points. Both techniques of the nonlinear least square adjustment either using the Gauss-Newton or Levenberg – Marquardt are applied in two 3D resection experiments. In both numerical methods, the results converged steadily to the global minimum using the proposed angular resection even with improper starting values. However, applying the Levenberg – Marquardt method proved to reach the global minimum solution in all the challenging situations and outperformed the Gauss-Newton method.

Early Recurrent Dislocation and Dissociation of Bipolar Cup: Two Intricate Cases and Review of Literature.

Background: Bipolar hemiarthroplasty (BHA) is one of the best options to treat femoral neck fracture in elderly patients. However, the early dislocation problem also happens in the bipolar cup. Furthermore, BHA has a specific implant-related complication, namely bipolar cup dissociation, in which the femoral head dissociates from the polyethylene (PE) liner. Early dislocation and dissociation are relatively uncommon but devastating complications and almost always lead to reoperation with revision of the prosthesis. Dislocation and dissociation of the bipolar cup can be challenging to manage and easily turn to recurrent episodes in case of improper handling. We hope to find out the best strategy for treatment early recurrent dislocation and dissociation of the bipolar cup.Methods: We retrospectively reviewed 2 patients who had a history of early recurrent posterior dislocations following primary BHA from 2016 to 2019. What’s more, they had early recurrent bipolar cup dissociation during the attempt of closed reduction maneuvers within 3 months of the original surgery.Results: All the patients were treated with open reduction and revision of Bipolar hemiarthroplasty (BHA). One patient has a good outcome, and no recurrence of dislocation was recorded.Conclusion: Recognition of the risk of dislocation and dissociation of bipolar cup is essential to its prevention and treatment. Complete revision of bipolar parts and the femoral head may be the minimum solution necessary to avoid the early recurrence of bipolar cup dissociation. Further investigations are recommended to strengthen these results.

Developing a New Robust Swarm-Based Algorithm for Robot Analysis

Metaheuristics are incapable of analyzing robot problems without being enhanced, modified, or hybridized. Enhanced metaheuristics reported in other works of literature are problem-specific and often not suitable for analyzing other robot configurations. The parameters of standard particle swarm optimization (SPSO) were shown to be incapable of resolving robot optimization problems. A novel algorithm for robot kinematic analysis with enhanced parameters is hereby presented. The algorithm is capable of analyzing all the known robot configurations. This was achieved by studying the convergence behavior of PSO under various robot configurations, with a view of determining new PSO parameters for robot analysis and a suitable adaptive technique for parameter identification. Most of the parameters tested stagnated in the vicinity of strong local minimizers. A few parameters escaped stagnation but were incapable of finding the global minimum solution, this is undesirable because accuracy is an important criterion for robot analysis and control. The algorithm was trained to identify stagnating solutions. The algorithm proposed herein was found to compete favorably with other algorithms reported in the literature. There is a great potential of further expanding the findings herein for dynamic parameter identification.

PENERAPAN MODIFIED VOGEL'S APPROXIMATION METHOD (MVAM) UNTUK MEMINIMUMKAN BIAYA TRANSPORTASI (STUDI KASUS: PABRIK TAHU TAUFIK)

This research aims to determine the tofu product distribution transportation model at Taufik Tofu Factory. This type of research is applied research with a quantitative approach. The method used to solve transportation problems in this study is the Modified Vogel’s Approximation Method (MVAM) and optimized using the Modified Distribution (MODI) method. The results of this study indicate that the application of MVAM at the Taufik Tofu Factory provides a more minimum solution for calculating transportation costs, both in equilibrium and non-equilibrium problems. MVAM provides the same transportation costs as the optimal solution for the simplex method, which is equal to Rp1.164.911,00 for equilibrium problems and Rp2.176.838,00 for unequal problems. These results have been tested for optimism using MODI and better than the real cost calculations issued by the factory.

Framework and Evolution of Task Orientation using Fuzzy and GA

The process of selecting employees in any organization is done by mostly old ways. There is a lack of hypothetical support in this method, and error is possible in the result. In the research paper presented, we have outlined the recruitment and selection process of employees with the help of Fuzzy Triangular Number. The selection process given here is completely different from the methods given earlier. To find the ideal employee here, we use the modified method of solving assignment problem. Using certain linguistic variables, we use robust ranking method here. The solution obtained from this method is minimized using the revised approach of assignment. This modified approach is simpler than the Hungarian method previously used by other authors. Using this modified solution, the minimum solution is achieved by reducing the number of recurring assignments. After assignment process, finally selection of employees is given using criterion of GA.

Machine learning landscapes and predictions for patient outcomes

The theory and computational tools developed to interpret and explore energy landscapes in molecular science are applied to the landscapes defined by local minima for neural networks. These machine learning landscapes correspond to fits of training data, where the inputs are vital signs and laboratory measurements for a database of patients, and the objective is to predict a clinical outcome. In this contribution, we test the predictions obtained by fitting to single measurements, and then to combinations of between 2 and 10 different patient medical data items. The effect of including measurements over different time intervals from the 48 h period in question is analysed, and the most recent values are found to be the most important. We also compare results obtained for neural networks as a function of the number of hidden nodes, and for different values of a regularization parameter. The predictions are compared with an alternative convex fitting function, and a strong correlation is observed. The dependence of these results on the patients randomly selected for training and testing decreases systematically with the size of the database available. The machine learning landscapes defined by neural network fits in this investigation have single-funnel character, which probably explains why it is relatively straightforward to obtain the global minimum solution, or a fit that behaves similarly to this optimal parameterization.