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In this study, a novel framework for the flight control of a morphing unmanned aerial vehicle (UAV) based on linear parameter-varying (LPV) methods is proposed. A high-fidelity nonlinear model and LPV model of an asymmetric variable-span morphing UAV were obtained using the NASA generic transport model. The left and right wing span variation ratios were decomposed into symmetric and asymmetric morphing parameters, which were then used as the scheduling parameter and the control input, respectively. LPV-based control augmentation systems were designed to track the normal acceleration, angle of sideslip, and roll rate commands. The span morphing strategy was investigated considering the effects of morphing on various factors to aid the intended maneuver. Autopilots were designed using LPV methods to track commands for airspeed, altitude, angle of sideslip, and roll angle. A nonlinear guidance law was coupled with the autopilots for three-dimensional trajectory tracking. A numerical simulation was performed to demonstrate the effectiveness of the proposed scheme.

Owing to the increasing construction of new buildings, the increase in the emission of formaldehyde and volatile organic compounds, which are emitted as indoor air pollutants, is causing adverse effects on the human body, including life-threatening diseases such as cancer. A gas sensor was fabricated and used to measure and monitor this phenomenon. An alumina substrate with Au, Pt, and Zn layers formed on the electrode was used for the gas sensor fabrication, which was then classified into two types, A and B, representing the graphene spin coating before and after the heat treatment, respectively. Ultrasonication was performed in a 0.01 M aqueous solution, and the variation in the sensing accuracy of the target gas with the operating temperature and conditions was investigated. As a result, compared to the ZnO sensor showing excellent sensing characteristics at 350 °C, it exhibited excellent sensing characteristics even at a low temperature of 150 °C, 200 °C, and 250 °C.

With the development of the internet of things (IoT), the power grid has become intelligent using massive IoT sensors, such as smart meters. Generally, installed smart meters can collect large amounts of data to improve grid visibility and situational awareness. However, the limited storage and communication capacities can restrain their infrastructure in the IoT environment. To alleviate these problems, efficient and various compression techniques are required. Deep learning-based compression techniques such as auto-encoders (AEs) have recently been deployed for this purpose. However, the compression performance of the existing models can be limited when the spectral properties of high-frequency sampled power data are widely varying over time. This paper proposes an AE compression model, based on a frequency selection method, which improves the reconstruction quality while maintaining the compression ratio (CR). For efficient data compression, the proposed method selectively applies customized compression models, depending on the spectral properties of the corresponding time windows. The framework of the proposed method involves two primary steps: (i) division of the power data into a series of time windows with specified spectral properties (high-frequency, medium-frequency, and low-frequency dominance) and (ii) separate training and selective application of the AE models, which prepares them for the power data compression that best suits the characteristics of each frequency. In simulations on the Dutch residential energy dataset, the frequency-selective AE model shows significantly higher reconstruction performance than the existing model with the same CR. In addition, the proposed model reduces the computational complexity involved in the analysis of the learning process.

A strain-based load identification model for beam structures subjected to multiple loads is presented. The number of sensors for the load identification model is the same as the number of load conditions acting on a beam structure. In the model, the contribution of each load to the strains measured by strain sensors is defined. In this paper, the longitudinal strains measured from multiplexed fiber Bragg grating (FBG) strain sensors are used in the load identification. To avoid the dependency on the selection of locations for FBG sensors installed on a beam structure, the measured strain is expressed by a general form of a strain sensing model defined by superimposing the distribution shapes for strains from multiple loads. Numerical simulation is conducted to verify the model. Then, the load identification model is applied to monitoring of applied loads on a 4 m-long steel beam subjected to two concentrated loads. In the experiment, seven FBG sensors and nine electrical strain gages (ESGs) were installed on the surface of the bottom flange. The experimental results indicate a good agreement between estimated loadings from the model and the loads applied by a hydraulic jack.

In a ground anchor system, cables or tendons connected to a bearing plate are used for stabilization of slopes. Then, the stability of a slope is dependent on maintaining the tension levels in the cables. So far, no research on a strain-based field sensing system for ground anchors has been reported. Therefore, in this study, a practical monitoring system for long-term sensing of tension levels in tendons for anchor-reinforced slopes is proposed. The system for anchor-reinforced slopes is composed of: (1) load cells based on vibrating wire strain gauges (VWSGs), (2) wireless sensor nodes which receive and process the signals from load cells and then transmit the result to a master node through local area communication, (3) master nodes which transmit the data sent from sensor nodes to the server through mobile communication, and (4) a server located at the base station. The system was applied to field sensing of ground anchors in the 62 m-long and 26 m-high slope at the side of the highway. Based on the long-term monitoring, the safety of the anchor-reinforced slope can be secured by the timely applications of re-tensioning processes in tendons.

In this research, we developed a portable, three-electrode electrochemical amperometric analyzer that can transmit data to a PC or a tablet via Bluetooth communication. We performed experiments using an indium tin oxide (ITO) glass electrode to confirm the performance and reliability of the analyzer. The proposed analyzer uses a current-to-voltage (I/V) converter to convert the current generated by the reduction-oxidation (redox) reaction of the buffer solution to a voltage signal. This signal is then digitized by the processor. The configuration of the power and ground of the printed circuit board (PCB) layer is divided into digital and analog parts to minimize the noise interference of each part. The proposed analyzer occupies an area of 5.9 × 3.25 cm2 with a current resolution of 0.4 nA. A potential of 0~2.1 V can be applied between the working and the counter electrodes. The results of this study showed the accuracy of the proposed analyzer by measuring the Ruthenium(III) chloride ( Ru III ) concentration in 10 mM phosphate-buffered saline (PBS) solution with a pH of 7.4. The measured data can be transmitted to a PC or a mobile such as a smartphone or a tablet PC using the included Bluetooth module. The proposed analyzer uses a 3.7 V, 120 mAh lithium polymer battery and can be operated for 60 min when fully charged, including data processing and wireless communication.