OPTIMASI MOTORIK KASAR ANAK MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION

Motorik kasar anak merupakan keterampilan anak untuk menggerakkan badan atau bagian tubuhnya secara harmonis. Perkembangan motorik yang terlambat berbahaya bagi penyesuaian sosial dan pribadi anak. Motorik kasar anak berhubungan dengan pertumbuhan anak seperti gerak badan berdasarkan berat badan, gerak kaki berdasarkan tinggi badan, gerak kepala berdasarkan lingkar kepala dan gerak tangan berdasarkan lingkar lengan. Perhitungan selama ini hanya dapat memperkirakan jumlah gerakan kepala, tangan, badan dan kaki saja. Sedangkan menurut Departemen Kesehatan Republik Indonesia, terdapat 12 gerakan yang harus diperhatikan. Pada penelitian ini digunakan algoritma particle swarm optimization (PSO) yang merupakan algoritma berbasis populasi yang mengeksploitasi individu dalam pencarian. Berdasarkan pengujian parameter yang telah dilakukan, didapatkan parameter particle swarm optimization yang paling optimal untuk mendapatkan gerakan motorik kasar anak adalah dengan menginputkan partikel sebesar 20, jumlah maksimal iterasi sebesar 25, wmin sebesar 0.4, wmax sebesar 0.9, c1min sebesar 0.5 , c1max sebesar 2.5, c2min sebesar 0.5 dan c2max sebesar 2.5. Hasil akhir berhasil didapatkan jumlah gerak miring kekiri, miring kekanan, memutar, menoleh kekiri, menoleh kekanan, mengangkat keatas, renggang buka tutup, silang kedalam depan, mengangkat turunkan, tekuk lutut, silang ganti-ganti, dan mengangkat turunkan

OPTIMASI MOTORIK KASAR ANAK MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION

Motorik kasar anak merupakan keterampilan anak untuk menggerakkan badan atau bagian tubuhnya secara harmonis. Perkembangan motorik yang terlambat berbahaya bagi penyesuaian sosial dan pribadi anak. Motorik kasar anak berhubungan dengan pertumbuhan anak seperti gerak badan berdasarkan berat badan, gerak kaki berdasarkan tinggi badan, gerak kepala berdasarkan lingkar kepala dan gerak tangan berdasarkan lingkar lengan. Perhitungan selama ini hanya dapat memperkirakan jumlah gerakan kepala, tangan, badan dan kaki saja. Sedangkan menurut Departemen Kesehatan Republik Indonesia, terdapat 12 gerakan yang harus diperhatikan. Pada penelitian ini digunakan algoritma particle swarm optimization (PSO) yang merupakan algoritma berbasis populasi yang mengeksploitasi individu dalam pencarian. Berdasarkan pengujian parameter yang telah dilakukan, didapatkan parameter particle swarm optimization yang paling optimal untuk mendapatkan gerakan motorik kasar anak adalah dengan menginputkan partikel sebesar 20, jumlah maksimal iterasi sebesar 25, wminsebesar 0.4, wmaxsebesar 0.9, c1min sebesar 0.5 , c1max sebesar 2.5, c2min sebesar 0.5 dan c2max sebesar 2.5. Hasil akhir berhasil didapatkan jumlah gerak miring kekiri, miring kekanan, memutar, menoleh kekiri, menoleh kekanan, mengangkat keatas, renggang buka tutup, silang kedalam depan, mengangkat turunkan, tekuk lutut, silang ganti-ganti, dan mengangkat turunka

Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics

Quantum computing is powerful because unitary operators describing the time-evolution of a quantum system have exponential size in terms of the number of qubits present in the system. We develop a new "Singular value transformation" algorithm capable of harnessing this exponential advantage, that can apply polynomial transformations to the singular values of a block of a unitary, generalizing the optimal Hamiltonian simulation results of Low and Chuang. The proposed quantum circuits have a very simple structure, often give rise to optimal algorithms and have appealing constant factors, while usually only use a constant number of ancilla qubits. We show that singular value transformation leads to novel algorithms. We give an efficient solution to a certain "non-commutative" measurement problem and propose a new method for singular value estimation. We also show how to exponentially improve the complexity of implementing fractional queries to unitaries with a gapped spectrum. Finally, as a quantum machine learning application we show how to efficiently implement principal component regression. "Singular value transformation" is conceptually simple and efficient, and leads to a unified framework of quantum algorithms incorporating a variety of quantum speed-ups. We illustrate this by showing how it generalizes a number of prominent quantum algorithms, including: optimal Hamiltonian simulation, implementing the Moore-Penrose pseudoinverse with exponential precision, fixed-point amplitude amplification, robust oblivious amplitude amplification, fast QMA amplification, fast quantum OR lemma, certain quantum walk results and several quantum machine learning algorithms. In order to exploit the strengths of the presented method it is useful to know its limitations too, therefore we also prove a lower bound on the efficiency of singular value transformation, which often gives optimal bounds.Comment: 67 pages, 1 figur

Instruction-Level Distributed Processing

has emphasized instruction-level parallelism, which improves performance by increasing the number of instructions per cycle. In striving for such parallelism, researchers have taken microarchitectures from pipelining to superscalar processing, pushing toward increasingly parallel processors. They have concentrated on wider instruction fetch, higher instruction issue rates, larger instruction windows, and increasing use of prediction and speculation. In short, researchers have exploited advances in chip technology to develop complex, hardware-intensive processors. Benefiting from ever-increasing transistor budgets and taking a highly optimized, “big-compile ” view of software, microarchitecture researchers made significant progress through the mid-1990s. More recently, though, researchers have seemingly reduced the problem to finding ways of consuming transistors, resulting in hardware-intensive, complex processors. The complexity is not just in critical path lengths and transistor counts: There is high intellectual complexity in the increasingly intricate schemes for squeezing performance out of second- and third-order effects. Substantial shifts in hardware technology and software applications will lead to general-purpose microarchitectures composed of small, simple, interconnected processing elements, running at very high clock frequencies. A hidden layer of implementationspecific software—co-designed with the hardware— will help manage the distributed hardware resources to use power efficiently and to dynamically optimize executing threads based on observed instruction dependencies and communication. COVER FEATUR

Appears in the Proceedings of the 12th International Symposium on High-Performance Computer Architecture (HPCA-12), Febryary 2006.

Order-Recording and Data race detection ∗ Chip-multiprocessors are becoming the dominant vehicle for general-purpose processing, and parallel software will be needed to effectively utilize them. This parallel software is notoriously prone to synchronization bugs, which are often difficult to detect and repeat for debugging. While data race detection and order-recording for deterministic replay are useful in debugging such problems, only order-recording schemes are lightweight, whereas data race detection support scales poorly and degrades performance significantly. This paper presents our CORD (Cost-effective Order-Recording and Data race detection) mechanism. It is similar in cost to prior order-recording mechanisms, but costs considerably less then prior schemes for data race detection. CORD also has a negligible performance overhead (0.4 % on average) and detects most dynamic manifestations of synchronization problems (77 % on average). Overall, CORD is fast enough to run always (even in performance-sensitive production runs) and provides the support programmers need to deal with the complexities of writing, debugging, and maintaining parallel software for future multi-threaded and multi-core machines. 1