Balancing mixed-model assembly line to reduce work overload in a multi-level production system

Generating the optimal production schedule for an assembly line, which will balance the workload at all the production stages, is a difficult task considering a variety of practical constraints. Varying customer demand is an important factor to be considered when designing an assembly line. In order to respond to varying customer demand, many companies are attempting to make their production system more flexible/agile or adaptable to change. Due to the volatile nature of market, companies cannot afford to manufacture same type of product for long period of time and neither can maintain high inventory level; to tackle this problem we propose a new approach of balancing mixed-model assembly line in a multi-level production system. The emphasis is on incorporating the effect of set-up times of lower production levels on the final assembly schedule. This will facilitate stabilized workload among and across the stations and effectively balance the production schedule at all production stages. As a result, the proposed model assures that workloads are balanced and setup times are reduced to such an extent that WIP and overall inventories are kept to a low level

Self-organizing maps: a tool to ascertain taxonomic relatedness based on features derived from 16S rDNA sequence

Exploitation of microbial wealth, of which almost 95% or more is still unexplored, is a growing need. The taxonomic placements of a new isolate based on phenotypic characteristics are now being supported by information preserved in the 16S rRNA gene. However, the analysis of 16S rDNA sequences retrieved from metagenome, by the available bioinformatics tools, is subject to limitations. In this study, the occurrences of nucleotide features in 16S rDNA sequences have been used to ascertain the taxonomic placement of organisms. The tetra- and penta-nucleotide features were extracted from the training data set of the 16S rDNA sequence, and was subjected to an artificial neural network (ANN) based tool known as self-organizing map (SOM), which helped in visualization of unsupervised classification. For selection of significant features, principal component analysis (PCA) or curvilinear component analysis (CCA) was applied. The SOM along with these techniques could discriminate the sample sequences with more than 90% accuracy, highlighting the relevance of features. To ascertain the confidence level in the developed classification approach, the test data set was specifically evaluated for Thiobacillus, with Acidiphilium, Paracocus and Starkeya, which are taxonomically reassigned. The evaluation proved the excellent generalization capability of the developed tool. The topology of genera in SOM supported the conventional chemo-biochemical classification reported in the Bergey manual

A validation of the Oswestry Spinal Risk Index

Purpose The purpose of this study was to validate the Oswestry Spinal Risk Index (OSRI) in an external population. The OSRI predicts survival in patients with metastatic spinal cord compression (MSCC). Methods We analysed the data of 100 patients undergoing surgical intervention for MSCC at a tertiary spinal unit and recorded the primary tumour pathology and Karnofsky performance status to calculate the OSRI. Logistic regression models and survival plots were applied to the data in accordance with the original paper. Results Lower OSRI scores predicted longer survival. The OSRI score predicted survival accurately in 74% of cases (p = 0.004). Conclusions Our study has found that the OSRI is a significant predictor of survival at levels similar to those of the original authors and is a useful and simple tool in aiding complex decision making in patients presenting with MSC

A 16-nm SoC for Noise-Robust Speech and NLP Edge AI Inference With Bayesian Sound Source Separation and Attention-Based DNNs

The proliferation of personal artificial intelligence (AI) -assistant technologies with speech-based conversational AI interfaces is driving the exponential growth in the consumer Internet of Things (IoT) market. As these technologies are being applied to keyword spotting (KWS), automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech (TTS) applications, it is of paramount importance that they provide uncompromising performance for context learning in long sequences, which is a key benefit of the attention mechanism, and that they work seamlessly in polyphonic environments. In this work, we present a 25-mm $^2$ system-on-chip (SoC) in 16-nm FinFET technology, codenamed SM6, which executes end-to-end speech-enhancing attention-based ASR and NLP workloads. The SoC includes: 1) FlexASR, a highly reconfigurable NLP inference processor optimized for whole-model acceleration of bidirectional attention-based sequence-to-sequence (seq2seq) deep neural networks (DNNs); 2) a Markov random field source separation engine (MSSE), a probabilistic graphical model accelerator for unsupervised inference via Gibbs sampling, used for sound source separation; 3) a dual-core Arm Cortex A53 CPU cluster, which provides on-demand single Instruction/multiple data (SIMD) fast fourier transform (FFT) processing and performs various application logic (e.g., expectation–maximization (EM) algorithm and 8-bit floating-point (FP8) quantization); and 4) an always-on M0 subsystem for audio detection and power management. Measurement results demonstrate the efficiency ranges of 2.6–7.8 TFLOPs/W and 4.33–17.6 Gsamples/s/W for FlexASR and MSSE, respectively; MSSE denoising performance allowing 6 $\times$ smaller ASR model to be stored on-chip with negligible accuracy loss; and 2.24-mJ energy consumption while achieving real-time throughput, end-to-end, and per-frame ASR latencies of 18 ms

Coordination of Mobile Mules via Facility Location Strategies

In this paper, we study the problem of wireless sensor network (WSN) maintenance using mobile entities called mules. The mules are deployed in the area of the WSN in such a way that would minimize the time it takes them to reach a failed sensor and fix it. The mules must constantly optimize their collective deployment to account for occupied mules. The objective is to define the optimal deployment and task allocation strategy for the mules, so that the sensors' downtime and the mules' traveling distance are minimized. Our solutions are inspired by research in the field of computational geometry and the design of our algorithms is based on state of the art approximation algorithms for the classical problem of facility location. Our empirical results demonstrate how cooperation enhances the team's performance, and indicate that a combination of k-Median based deployment with closest-available task allocation provides the best results in terms of minimizing the sensors' downtime but is inefficient in terms of the mules' travel distance. A k-Centroid based deployment produces good results in both criteria.Comment: 12 pages, 6 figures, conferenc

Intraoperative manufacturing of patient specific instrumentation for shoulder arthroplasty: a novel mechatronic approach

Optimal orthopaedic implant placement is a major contributing factor to the long term success of all common joint arthroplasty procedures. Devices such as three-dimensional (3D) printed, bespoke guides and orthopaedic robots are extensively described in the literature and have been shown to enhance prosthesis placement accuracy. These technologies, however, have significant drawbacks, such as logistical and temporal inefficiency, high cost, cumbersome nature and difficult theatre integration. A new technology for the rapid intraoperative production of patient specific instrumentation, which overcomes many of the disadvantages of existing technologies, is presented here. The technology comprises a reusable table side machine, bespoke software and a disposable element comprising a region of standard geometry and a body of mouldable material. Anatomical data from Computed Tomography (CT) scans of 10 human scapulae was collected and, in each case, the optimal glenoid guidewire position was digitally planned and recorded. The achieved accuracy compared to the preoperative bespoke plan was measured in all glenoids, from both a conventional group and a guided group. The technology was successfully able to intraoperatively produce sterile, patient specific guides according to a pre-operative plan in 5 minutes, with no additional manufacturing required prior to surgery. Additionally, the average guide wire placement accuracy was 1.58 mm and 6.82◦ degrees in the manual group, and 0.55 mm and 1.76◦ degrees in the guided group, also demonstrating a statistically significant improvement

Ballistic nanofriction

Sliding parts in nanosystems such as Nano ElectroMechanical Systems (NEMS) and nanomotors, increasingly involve large speeds, and rotations as well as translations of the moving surfaces; yet, the physics of high speed nanoscale friction is so far unexplored. Here, by simulating the motion of drifting and of kicked Au clusters on graphite - a workhorse system of experimental relevance -- we demonstrate and characterize a novel "ballistic" friction regime at high speed, separate from drift at low speed. The temperature dependence of the cluster slip distance and time, measuring friction, is opposite in these two regimes, consistent with theory. Crucial to both regimes is the interplay of rotations and translations, shown to be correlated in slow drift but anticorrelated in fast sliding. Despite these differences, we find the velocity dependence of ballistic friction to be, like drift, viscous

Thermal activation in atomic friction: revisiting the theoretical analysis

The effect of thermal activation on atomic-scale friction is often described in the framework of the Prandtl–Tomlinson model. Accurate use of this model relies on parameters that describe the shape of the corrugation potential β and the transition attempt frequency f0. We show that the commonly used form of β for a sinusoidal corrugation potential can lead to underestimation of friction, and that the attempt frequency is not, as is usually assumed, a constant value, but rather varies as the energy landscape evolves. We partially resolve these issues by demonstrating that numerical results can be captured by a model with a fitted β and using harmonic transition state theory to develop a variable form of the attempt frequency. We incorporate these developments into a more accurate and generally applicable expression relating friction to temperature and velocity. Finally, by using a master equation approach, we verify the improved analytical model is accurate in its expected regime of validity. (Some figures may appear in colour only in the online journal) 1

Spatial regularity of InAs-GaAs quantum dots: quantifying the dependence of lateral ordering on growth rate.

The lateral ordering of arrays of self-assembled InAs-GaAs quantum dots (QDs) has been quantified as a function of growth rate, using the Hopkins-Skellam index (HSI). Coherent QD arrays have a spatial distribution which is neither random nor ordered, but intermediate. The lateral ordering improves as the growth rate is increased and can be explained by more spatially regular nucleation as the QD density increases. By contrast, large and irregular 3D islands are distributed randomly on the surface. This is consistent with a random selection of the mature QDs relaxing by dislocation nucleation at a later stage in the growth, independently of each QD's surroundings. In addition we explore the statistical variability of the HSI as a function of the number N of spatial points analysed, and we recommend N > 10(3) to reliably distinguish random from ordered arrays