Impedance adaptation for optimal robot–environment interaction

In this paper, impedance adaptation is investigated for robots interacting with unknown environments. Impedance control is employed for the physical interaction between robots and environments, subject to unknown and uncertain environments dynamics. The unknown environments are described as linear systems with unknown dynamics, based on which the desired impedance model is obtained. A cost function that measures the tracking error and interaction force is defined, and the critical impedance parameters are found to minimize it. Without requiring the information of the environments dynamics, the proposed impedance adaptation is feasible in a large number of applications where robots physically interact with unknown environments. The validity of the proposed method is verified through simulation studies

Design of a General-Purpose MIMO Predictor with Neural Networks

A new multi-step predictor for multiple-input, multiple-output (MIMO) systems is proposed. The output prediction of such a system is represented as a mapping from its historical data and future inputs to future outputs. A neural network is designed to learn the mapping without re quiring a priori knowledge of the parameters and structure of the system. The major problem in de veloping such a predictor is how to train the neural network. In case of the back propagation algorithm, the network is trained by using the network's output error which is not known due to the unknown predicted future system outputs. To overcome this problem, the concept of updating, in stead of training, a neural network is introduced and verified with simulations. The predictor then uses only the system's historical data to update the configuration of the neural network and always works in a closed loop. If each node can only handle scalar operations, emulation of an MIMO mapping requires the neural network to be excessively large, and it is difficult to specify some known coupling effects of the predicted system. So, we propose a vector-structured, multilayer perceptron for the predictor design. MIMO linear, nonlinear, time-invariant, and time-varying systems are tested via simulation, and all showed very promising performances.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68861/2/10.1177_1045389X9400500206.pd

Backpropagation training in adaptive quantum networks

We introduce a robust, error-tolerant adaptive training algorithm for generalized learning paradigms in high-dimensional superposed quantum networks, or \emph{adaptive quantum networks}. The formalized procedure applies standard backpropagation training across a coherent ensemble of discrete topological configurations of individual neural networks, each of which is formally merged into appropriate linear superposition within a predefined, decoherence-free subspace. Quantum parallelism facilitates simultaneous training and revision of the system within this coherent state space, resulting in accelerated convergence to a stable network attractor under consequent iteration of the implemented backpropagation algorithm. Parallel evolution of linear superposed networks incorporating backpropagation training provides quantitative, numerical indications for optimization of both single-neuron activation functions and optimal reconfiguration of whole-network quantum structure.Comment: Talk presented at "Quantum Structures - 2008", Gdansk, Polan

Scalable Massively Parallel Artificial Neural Networks

There is renewed interest in computational intelligence, due to advances in algorithms, neuroscience, and computer hardware. In addition there is enormous interest in autonomous vehicles (air, ground, and sea) and robotics, which need significant onboard intelligence. Work in this area could not only lead to better understanding of the human brain but also very useful engineering applications. The functioning of the human brain is not well understood, but enormous progress has been made in understanding it and, in particular, the neocortex. There are many reasons to develop models of the brain. Artificial Neural Networks (ANN), one type of model, can be very effective for pattern recognition, function approximation, scientific classification, control, and the analysis of time series data. ANNs often use the back-propagation algorithm for training, and can require large training times especially for large networks, but there are many other types of ANNs. Once the network is trained for a particular problem, however, it can produce results in a very short time. Parallelization of ANNs could drastically reduce the training time. An object-oriented, massively-parallel ANN (Artificial Neural Network) software package SPANN (Scalable Parallel Artificial Neural Network) has been developed and is described here. MPI was use

IMPLEMENTASI PEMBERIAN GANTI KERUGIAN LAYANAN PAKET DI PT TIKI JALUR NUGRAHA EKAKURIR (JNE) CABANG SURAKARTA DITINJAU DARI UNDANG-UNDANG NOMOR 38 TAHUN 2009 TENTANG POS

ABSTRAK Yusuf Bintang Syaifinuha, E0012414. 2016. IMPLEMENTASI PEMBERIAN GANTI KERUGIAN LAYANAN PAKET DI PT TIKI JALUR NUGRAHA EKAKURIR (JNE) CABANG SURAKARTA DITINJAU DARI UNDANG-UNDANG NOMOR 38 TAHUN 2009 TENTANG POS. Fakultas Hukum Universitas Sebelas Maret Surakarata. Penelitian ini bertujuan untuk mengetahui implementasi pemberian ganti kerugian di PT Tiki Jalur Nugraha Ekakurir (JNE) Cabang Surakarta ditinjau dari UU Pos dan untuk mengetahui cara penyelesaian sengketa yang timbul dari implementasi pemberian ganti kerugian di PT Tiki Jalur Nugraha Ekakurir (JNE) Cabang Surakarta. Metode penelitian yang digunakan dalam penelitian ini adalah deskriptif kualitatif. Jenis penelitian ini adalah penelitian empiris. Data yang digunakan terdiri dari dua data yaitu data primer dan data sekunder. Teknik pengumpulan data adalah dengan metode wawancara dan studi pustaka. Berdasarkan hasil analisis penelitian, dapat diambil kesimpulan bahwa implementasi pemberian ganti kerugian di JNE kurang sesuai dengan Pasal 28 UU Pos karena jenis ganti kerugian hanya untuk kehilangan kiriman dan kerusakan isi kiriman. Pengirim yang mengajukan klaim ganti kerugian harus memenuhi syarat administrasi yang telah ditetapkan oleh JNE. Nilai ganti kerugian yang diberikan JNE adalah 10 kali biaya pengiriman atau sesuai harga barang yang hilang dan/atau rusak jika menggunakan asuransi. JNE memilih upaya hukum diluar pengadilan (nonlitigasi) berupa negosiasi dalam menyelesaikan sengketa yang terjadi dalam implementasi pemberian ganti kerugian. Kata kunci :Perjanjian, Wanprestasi, Implementasi ganti kerugian