A dual-responsive humidity sensitive-based cellulose paper: A fabrication of smart strip from sustainable nanoclay and graphite-beeswax composite

A highly humidity-responsive paper can detect moisture or OH-attached molecules at the sub-micron or nano level range. Applying hydrophilic nanoclay as an active material for water sensing would reduce the fabrication cost while remaining environmentally friendly. In this communication, we report the preparation of a nanoclay-inspired dual-responsive smart film that can signal humidity levels from mechanical and electrical responses. The paper will bend as the nanoclay layer swells whereas the resulting bending strain will trigger the electrical resistance changes in the hydrophobic electrically conductive graphite-beeswax layer

Menyelesaikan masalah perancangan jujukan pemasangan menggunakan algoritma penapis Kalman diselakukan

Perancangan jujukan pemasangan (Assembly Sequence Planning - ASP) memainkan peranan penting dalam reka bentuk dan pembuatan produk. Jujukan pemasangan mempengaruhi keseluruhan produktiviti kerana ia menentukan kepantasan dan ketepatan produk itu dipasang. Objektif utama ASP adalah untuk menentukan jujukan pemasangan komponen untuk memendekkan masa pemasangan atau menjimatkan kos pemasangan. Walau bagaimanapun, ASP juga dikenali sebagai masalah pengoptimuman gabungan klasik yang sukar. Dengan peningkatan bilangan komponen bagi sesuatu produk, ASP menjadi lebih sukar dan algoritma berasaskan grafik tradisional tidak dapat menyelesaikannya dengan berkesan. Terdapat pelbagai metaheuristik yang wujud pada masa kini. Walau bagaimanapun, tidak semua metaheuristik dibangunkan untuk beroperasi di ruang carian diskret. Salah satu contoh algoritma metaheuristik ialah Kalman. Maka, bagi tujuan menyelesaikan masalah pengoptimuman gabungan (Combinatorial Optimization Problem - COP) yang diskret menggunakan metaheuristik serta menilai prestasi algoritma yang dicadangkan, satu kajian kes ASP telah dijalankan. Prestasi algoritma penapis Kalman diselakukan (Simulated Kalman Filter - SKF) lanjutan yang dinamakan penapis Kalman diselakukan binari (Binary Simulated Kalman Filter – BSKF), penapis Kalman diselakukan dimodulasi sudut (Angle Modulated Simulated Kalman Filter – AMSKF), dan penapis Kalman diselakukan dinilai jarak (Distance- Evaluated Simulated Kalman Filter - DESKF) dibandingkan dengan hasil kajian lalu yang menggunakan algoritma carian graviti binari (Binary Gravitational Search Algorithm - BGSA), algoritma pengoptimuman kerumunan zarah binari (Binary Particle Swarm Optimization - BPSO), algoritma carian graviti berbilang keadaan (Multi-State Gravitational Search Algorithm - MSGSA), algoritma carian graviti berbilang keadaan dengan peraturan tertanam (Multi-State Gravitational Search Algorithm with an Embedded Rule - MSGSAER), algoritma pengoptimuman kerumunan zarah berbilang keadaan (Multi-State Particle Swarm Optimization - MSPSO), dan algoritma pengoptimuman sekawan zarah berbilang keadaan dengan peraturan tertanam (Multi- State Particle Swarm Optimization with an Embedded Rule - MSPSOER) dalam menyelesaikan masalah ASP. Dengan menggunakan satu kajian kes ASP, hasil eksperimen menunjukkan AMSKF mengatasi BSKF, DESKF dan enam algoritma lain daripada kajian lalu dengan kelebihan sehingga 0.95% dalam mencari penyelesaian yang optimum

Global Optimum Distance Evaluated Particle Swarm Optimization for Combinatorial Optimization Problem

Based on the mechanism of Particle Swarm Optimization (PSO) measurement process, every particle estimates the global minimum/maximum. Particles communicate among them to update and improve the solution during the search process. However, the PSO is only capable to solve continuous numerical optimization problem. In order to solve discrete optimization problems, a new global optimum distance evaluated approach is proposed and combined with PSO. A set of traveling salesman problems (TSP) are used to evaluate the performance of the proposed global optimum distance evaluated PSO (GO-DEPSO). Based on the analysis of experimental results, we found that the proposed DEPSO is capable to solve discrete optimization problems using TSP

Solving assembly sequence planning using angle modulated simulated kalman filter

This paper presents an implementation of Simulated Kalman Filter (SKF) algorithm for optimizing an Assembly Sequence Planning (ASP) problem. The SKF search strategy contains three simple steps; predict-measure-estimate. The main objective of the ASP is to determine the sequence of component installation to shorten assembly time or save assembly costs. Initially, permutation sequence is generated to represent each agent. Each agent is then subjected to a precedence matrix constraint to produce feasible assembly sequence. Next, the Angle Modulated SKF (AMSKF) is proposed for solving ASP problem. The main idea of the angle modulated approach in solving combinatorial optimization problem is to use a function, g(x), to create a continuous signal. The performance of the proposed AMSKF is compared against previous works in solving ASP by applying BGSA, BPSO, and MSPSO. Using a case study of ASP, the results show that AMSKF outperformed all the algorithms in obtaining the best solution