Superhydrophobic pillar arrays, which can generate the droplet pancake bouncing phenomenon with reduced liquid-solid contact time, have huge application prospects in anti-icing of aircraft wings from freezing rain. However, the previously reported pillar arrays, suitable for obtaining pancake bouncing, have a diameter ≤100 μm and height-diameter ratio >10, which are difficult to fabricate over a large area. Here, we have systematically studied the influence of the dimension of the superhydrophobic pillar arrays on the bouncing dynamics of water droplets. We show that the typical pancake bouncing with 57.8% reduction in contact time with the surface was observed on the superhydrophobic pillar arrays with 1.05 mm diameter, 0.8 mm height, and 0.25 mm space. Such pillar arrays with millimeter diameter and <1 height-diameter ratio can be easily fabricated over large areas. Further, a simple replication-spraying method was developed for the large-area fabrication of the superhydrophobic pillar arrays to induce pancake bouncing. No sacrificial layer was needed to reduce the adhesion in the replication processes. Since the bouncing dynamics were rather sensitive to the space between the pillars, a method to control the contact time, bouncing shape, horizontal bouncing direction, and reversible switch between pancake bouncing and conventional bouncing was realized by adjusting the inclination angle of the shape memory polymer pillars.

Key performance indicators (KPIs) are critical for manufacturing operation management and continuous improvement (CI). In modern manufacturing systems, KPIs are defined as a set of metrics to reflect operation performance, such as efficiency, throughput, availability, from productivity, quality and maintenance perspectives. Through continuous monitoring and measurement of KPIs, meaningful quantification and identification of different aspects of operation activities can be obtained, which enable and direct CI efforts. A set of 34 KPIs has been introduced in ISO 22400. However, the KPIs in a manufacturing system are not independent, and they may have intrinsic mutual relationships. The goal of this paper is to introduce a multi-level structure for identification and analysis of KPIs and their intrinsic relationships in production systems. Specifically, through such a hierarchical structure, we define and layer KPIs into levels of basic KPIs, comprehensive KPIs and their supporting metrics, and use it to investigate the relationships and dependencies between KPIs. Such a study can provide a useful tool for manufacturing engineers and managers to measure and utilize KPIs for CI.

A badminton training system based on body sensor networks has been proposed. The system may recognize different badminton strokes of badminton players. A two-layer hidden Markov model (HMM) classification algorithm is proposed to recognize 14 types of badminton strokes. In the first layer, we use acceleration magnitude of the right wrist to determine a threshold to detect strokes, and then, the HMM is applied to filter out nonstroke motions. In the second layer, we adopt the HMM to classify all the strokes into 14 categories. Experimental results show that the two-layer HMM can achieve good recognition accuracy. The effectiveness and feasibility of the two-layer HMM classification algorithm have been verified in a comparison. Index Terms-Badminton stroke recognition, body sensor networks (BSN), hidden Markov model (HMM).

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Superhydrophobic conical pillars have great industrial application potential in, for example, anti-icing of aircraft wings and protecting high voltage transmission lines from freezing rain because of their droplet pancake bouncing phenomenon which is recognized to furthest reduce the liquidsolid contact time. However, there are still no methods that can large-scale fabricate robust

Purpose -The purpose of this paper is to develop a health monitoring system that can measure human vital signs and recognize human activity based on body sensor network (BSN). Design/methodology/approach -The system is mainly composed of electrocardiogram (ECG) signal collection node, blood oxygen signal collection node, inertial sensor node, receiving node and upper computer software. The three collection nodes collect ECG signals, blood oxygen signals and motion signals. And then collected signals are transmitted wirelessly to receiving node and analyzed by software in upper computer in real-time. Findings -Experiment results show that the system can simultaneously monitor human ECG, heart rate, pulse rate, SpO 2 and recognize human activity. A classifier based on coupled hidden Markov model (CHMM) is adopted to recognize human activity. The average recognition accuracy of CHMM classifier is 94.8 percent, which is higher than some existent methods, such as supported vector machine (SVM), C4.5 decision tree and naive Bayes classifier (NBC). Practical implications -The monitoring system may be used for falling detection, elderly care, postoperative care, rehabilitation training, sports training and other fields in the future. Originality/value -First, the system can measure human vital signs (ECG, blood pressure, pulse rate, SpO 2 , temperature, heart rate) and recognizes some specific simple or complex activities (sitting, lying, go boating, bicycle riding). Second, the researches of using CHMM for activity recognition based on BSN are extremely few. Consequently, the classifier based on CHMM is adopted to recognize activity with ideal recognition accuracies in this paper. Keywords

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