Multi-time-horizon Solar Forecasting Using Recurrent Neural Network

The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and increased cost of operation. This research paper proposes a unified architecture for multi-time-horizon predictions for short and long-term solar forecasting using Recurrent Neural Networks (RNN). The paper describes an end-to-end pipeline to implement the architecture along with the methods to test and validate the performance of the prediction model. The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon. The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid.Comment: Accepted at: IEEE Energy Conversion Congress and Exposition (ECCE 2018), 7 pages, 5 figures, code available: sakshi-mishra.github.i

Real grain shape analysis: characterization and generation of representative virtual grains. application to railway ballast

Grain shape significantly influences the mechanical properties of granular media. In order to explore this effect and to simulate realistic material morphology, we designed a method which well characterizes real grains shape. Starting from a representation of the particle surfaces as a points cloud, this paper presents a method to generate a set of virtual grains that are morphologically representative of real ballast grains. The model relies on a statistical modelling of the ballast grain morphology based on a dimensionality reduction approach (Proper Orthogonal Decomposition) leading to an optimal and nearly exhaustive shape characterization by extracting a hierarchy of shape functions that fully describe the grain sample. We will show the efficiency of the both characterizing and generating methods and describe their advantages, as well as a future outloo

Locomotion Control of a Compliant Legged Robot from Slow Walking to Fast Running Regular Paper

In this paper, we propose a locomotion control method for a compliant legged robot from slow walking to fast running. We also examine the energy efficiency of the compliant legged robot controlled by the proposed locomotion control method. Experimentally, we obtain the robot running speed of about 4.3m/s with the initial compliant leg length of 0.1m. In addition, we obtain very good energy efficiency. In the best case, the mechanical cost of transport(Cmt), known as an energy efficiency measure, is obtained at about 0.2. Comparing with the other energy efficient robots, our robot exhibits very good energy efficiency. © 2012 Kim et al.; licensee InTech.1

Designing High-Performance Fuzzy Controllers Combining IP Cores and Soft Processors

This paper presents a methodology to integrate a fuzzy coprocessor described in VHDL (VHSIC Hardware Description Language) to a soft processor embedded into an FPGA, which increases the throughput of the whole system, since the controller uses parallelism at the circuitry level for high-speed-demanding applications, the rest of the application can be written in C/C++. We used the ARM 32-bit soft processor, which allows sequential and parallel programming. The FLC coprocessor incorporates a tuning method that allows to manipulate the system response. We show experimental results using a fuzzy PD+I controller as the embedded coprocessor

Speedup of Interval Type 2 Fuzzy Logic Systems Based on GPU for Robot Navigation

As the number of rules and sample rate for type 2 fuzzy logic systems (T2FLSs) increases, the speed of calculations becomes a problem. The T2FLS has a large membership value of inherent algorithmic parallelism that modern CPU architectures do not exploit. In the T2FLS, many rules and algorithms can be speedup on a graphics processing unit (GPU) as long as the majority of computation a various stages and components are not dependent on each other. This paper demonstrates how to install interval type 2 fuzzy logic systems (IT2-FLSs) on the GPU and experiments for obstacle avoidance behavior of robot navigation. GPU-based calculations are high-performance solution and free up the CPU. The experimental results show that the performance of the GPU is many times faster than CPU

Vision based 3D Gesture Tracking using Augmented Reality and Virtual Reality for Improved Learning Applications

3D gesture recognition and tracking based augmented reality and virtual reality have become a big interest of research because of advanced technology in smartphones. By interacting with 3D objects in augmented reality and virtual reality, users get better understanding of the subject matter where there have been requirements of customized hardware support and overall experimental performance needs to be satisfactory. This research investigates currently various vision based 3D gestural architectures for augmented reality and virtual reality. The core goal of this research is to present analysis on methods, frameworks followed by experimental performance on recognition and tracking of hand gestures and interaction with virtual objects in smartphones. This research categorized experimental evaluation for existing methods in three categories, i.e. hardware requirement, documentation before actual experiment and datasets. These categories are expected to ensure robust validation for practical usage of 3D gesture tracking based on augmented reality and virtual reality. Hardware set up includes types of gloves, fingerprint and types of sensors. Documentation includes classroom setup manuals, questionaries, recordings for improvement and stress test application. Last part of experimental section includes usage of various datasets by existing research. The overall comprehensive illustration of various methods, frameworks and experimental aspects can significantly contribute to 3D gesture recognition and tracking based augmented reality and virtual reality.Peer reviewe

Validitas Prediktif Papi-Kostick dan Baum terhadap Pengendalian Emosi Karyawan

The availability of quality human resources was important for the continuity excellence of the company and for gaining competitive advantage in the company. It could be obtained by operationalizing development or placement as part of HR management practices based on a potential review. This led to the use of psychological tests. The accuracy of psychological tests was required in potential review in order to predict future behavior. HR practitioners and psychologists showed appreciation and confidence in psychological tests. However, it had not been fully supported by empirical evidence regarding the accuracy of the tests for selecting employees. The purpose of this study was to figure out the extent of both tools in predicting emotion control. The finding from 159 subjects showed that the activity factor PAPI-Kostick and crown of tree predicted the emotion control.Abstrak : Untuk kesinambungan kinerja perusahaan dan capaian keuntungan kompetitif, diperlukan manajemen SDM potential review. Potential review memerlukan peran alat tes psikologi yang akurat untuk memprediksi perilaku. Apresiasi dan kepercayaan psikolog pada alat tes psikologi belum didukung oleh bukti-bukti empiris (evidance based) mengenai hal tersebut. Tujuan penelitian ini untuk melihat sejauh mana PAPI-Kostick dan BAUM mampu memprediksi pengendalian emosi. Hasil penelitian pada 159 karyawan, menunjukkan faktor aktivitas PAPI-Kostick dan bagian mahkota pada BAUM dapat memprediksi pengendalian emosi. Sehingga efektivitas penggunaan kedua alat tes psikologi tersebut untuk mengases potensi pengendalian emosi karyawan

Speedup of Interval Type 2 Fuzzy Logic Systems Based on GPU for Robot Navigation

As the number of rules and sample rate for type 2 fuzzy logic systems (T2FLSs) increases, the speed of calculations becomes a problem. The T2FLS has a large membership value of inherent algorithmic parallelism that modern CPU architectures do not exploit. In the T2FLS, many rules and algorithms can be speedup on a graphics processing unit (GPU) as long as the majority of computation a various stages and components are not dependent on each other. This paper demonstrates how to install interval type 2 fuzzy logic systems (IT2-FLSs) on the GPU and experiments for obstacle avoidance behavior of robot navigation. GPUbased calculations are high-performance solution and free up the CPU. The experimental results show that the performance of the GPU is many times faster than CPU