An Extended Kalman Filtering Technique for Geodetic First Order Data Assimilation

Data assimilation allows merging of different sources of data to estimate possible states of a system as it evolves in time. This therefore supports the idea of combining classical observations with Global Positioning System (GPS) observations to improve the integrity of first order geodetic controls in Nigeria. Given that these geodetic controls, which were established using traditional techniques and whose algorithms are still in use, the task of optimizing the coordinate values of these monuments to improve efficiency and accuracy in conventional geodetic operations around Nigeria is still a challenge. This study introduces the Extended Kalman Filter (EKF) technique for the modeling of these observations and their uncertainties in addition to exogenous noise, which is handled by an approximate set-valued state estimator. The proposed EKF provides a feasible linearization process in merging classical and GPS data collection modes as shown in our study. For each discrete time in the analysis step, it employs the Kalman gain computation, which attempts to weigh and balance uncertainties between the estimate and observation before proceeding to the analysis step. In this setup, the EKF constrains the system state in order to balance and strengthen the integrity of these first order monuments. The relationship of the derived system state with GPS coordinates (R2 = 0.85) and classical observations (R2 = 0.92) over Nigeria using a multi linear regression analysis is considerably strong. This outcome provides insight to the performance of the test algorithm and builds on the usefulness of data assimilation techniques in geodetic operations.

ALGORITMA GENERATE AND TEST UNTUK OPTIMALISASI MASALAH RUTE PERJALANAN

The common of urban city problem is a traffic jam, and it is heavily affect the public transportation service quality. Many cities in tropical countries suffer of heavy traffic jam due to the populaition of motorbike. According to Central Java Province Government, in 2014 the population of motorcycle is 439.418 units with the ownership ratio at 0.75 unit/person. Solo is one of typical middle size city in Central Java, Indonesia. Solo suffers from traffic jam in particular time of the day and it affect the quality of service of batik solo as the mass transportation service in Surakarta. This research would like to help the community get the optimal travel route with the fastest time, especially at the busiest times. The method used is Generate and Test algorithm. This method is a combination of Depth First Search and Backtracking. This research is conducted to get information about the influence of traffic volume, service road level and traffic jam level toward the time needed for Batik Solo Trans to find the optimal route. There is a route that been selected to be the most optimal route by considering the factors that influence it and the algorithm applied

Detecting Low-Intensity Fires in East Asia Using VIIRS Data: An Improved Contextual Algorithm

The Visible Infrared Imaging Radiometer Suite (VIIRS) fire detection algorithm mostly relies on thermal infrared channels that possess fixed or context-sensitive thresholds. The main channel used for fire identification is the mid-infrared channel, which has relatively low temperature saturation. Therefore, when the high temperature of a fire in this channel is used for initial screening, the threshold is relatively high. Although screening results are tested at different levels, few small fires will be lost under these strict test conditions. However, crop burning fires often occur in East Asia at a small scale and relatively low temperature, such that their radiative characteristics cannot meet the global threshold. Here, we propose a new weighted fire test algorithm to accurately detect small-scale fires based on differences in the sensitivity of test conditions to fire. This method reduces the problem of small fires being ignored because they do not meet some test conditions. Moreover, the adaptive threshold suitable for small fires is selected by bubble sorting according to the radiation characteristics of small fires. Our results indicate that the improved algorithm is more sensitive to small fires, with accuracies of 53.85% in summer and 73.53% in winter, representing an 18.69% increase in accuracy and a 28.91% decline in error rate.

Test algorithm to confirm the type of modern measuring devices for low absolute pressures and vacuum

The article provides information on the normative documentation applicable in the Russian Federation on the approval procedure of measuring devices, explains the procedure for carrying out tests for approval of the type of vacuum measuring devices at D. I. Mendeleyev Institute for Metrology VNIIM, summarized information about modern high-precision thermal (measuring range (1⋅10-2… 1.08⋅105) Pa, maximum permissible error of a measuring ±(10…50) %) and ionization (specified measuring range (1⋅10-7…1.3) Pa, maximum permissible error of a measuring ±(20…50) %) of vacuum gauges that have been tested for type approval since 2016. The information on the vacuum gauges contained in this article was published in the Federal Information Fund for Ensuring the Uniformity of Measurements.

BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain

Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. Most benchmarks are performance-based, to test algorithm performance under a wide set of conditions. There are also resource- and behaviour-based benchmarks to test the resource consumption and the behaviour of algorithms. In this article, we propose a novel behaviour-based benchmark toolbox: BIAS (Bias in Algorithms, Structural). This toolbox can detect structural bias per dimension and across dimension based on 39 statistical tests. Moreover, it predicts the type of structural bias using a Random Forest model. BIAS can be used to better understand and improve existing algorithms (removing bias) as well as to test novel algorithms for structural bias in an early phase of development. Experiments with a large set of generated structural bias scenarios show that BIAS was successful in identifying bias. In addition we also provide the results of BIAS on 432 existing state-of-the-art optimisation algorithms showing that different kinds of structural bias are present in these algorithms, mostly towards the centre of the objective space or showing discretization behaviour. The proposed toolbox is made available open-source and recommendations are provided for the sample size and hyper-parameters to be used when applying the toolbox on other algorithms.

BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain

Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. Most benchmarks are performance-based, to test algorithm performance under a wide set of conditions. There are also resource- and behaviour-based benchmarks to test the resource consumption and the behaviour of algorithms. In this article, we propose a novel behaviour-based benchmark toolbox: BIAS (Bias in Algorithms, Structural). This toolbox can detect structural bias per dimension and across dimension based on 39 statistical tests. Moreover, it predicts the type of structural bias using a Random Forest model. BIAS can be used to better understand and improve existing algorithms (removing bias) as well as to test novel algorithms for structural bias in an early phase of development. Experiments with a large set of generated structural bias scenarios show that BIAS was successful in identifying bias. In addition we also provide the results of BIAS on 432 existing state-of-the-art optimisation algorithms showing that different kinds of structural bias are present in these algorithms, mostly towards the centre of the objective space or showing discretization behaviour. The proposed toolbox is made available open-source and recommendations are provided for the sample size and hyper-parameters to be used when applying the toolbox on other algorithms.

Finite State Machine based Programmable Memory Built-in Self-Test

We propose a methodology to design a Finite State Machine(FSM)-based Programmable Memory Built-In Self Test (PMBIST) which includes a planned procedure for Memory BIST which has a controller to select a test algorithm from a fixed set of algorithms that are built in the memory BIST. In general, it is not possible to test all the different memory modules present in System-on-Chip (SoC) with a single Test algorithm. Subsequently it is desirable to have a programmable Memory BIST controller which can execute multiple test algorithms. The proposed Memory BIST controller is designed as a FSM (Finite State Machine) written in Verilog HDL and this scheme greatly simplifies the testing process and it achieves a good flexibility with smaller circuit size compared with Individual Testing designs. We have used March test algorithms like MATS+, March X, March C- to build the project.