• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.139-145
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• 2019

Precautionary detection of chemical warfare agents (CWAs) has been an important global issue mainly owing to their toxicity. To achieve proper detection, many studies have been conducted to develop sensitive gas sensors for CWAs. In particular, metal-oxide semi-conductors (MOS) have been investigated as promising sensing materials owing to their abundance in nature and excellent sensitivity. In this review, we mainly focus on various MOS-based gas sensors that have been fabricated for the detection of two specific CWA simulants, 2-chloroethyl ethyl sulfide (2-CEES) and dimethyl methyl phosphonate (DMMP), which are simulants of sulfur mustard and sarin, respectively. In the case of 2-CEES, we mainly discuss $CdSnO_3-$ and ZnO-based sensors and their reaction mechanisms. In addition, a method to improve the selectivity of ZnO-based sensors is mentioned. Various sensors and their sensing mechanisms have been introduced for the detection of DMMP. As the reaction with DMMP may directly affect the sensing properties of MOS, this paper includes previous studies on its poisoning effect. Finally, promising sensing materials for both gases are proposed.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1898-1899
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• 2011

In this paper, we propose an obstacle avoidance algorithm for a network-based autonomous mobile robot. The obstacle avoidance algorithm is based on the VFH (Vector Field Histogram) algorithm and delay-compensative methods with the VFH algorithm are proposed for the network-based robot that is a unified system composed of distributed environmental sensors, mobile actuators, and the VFH controller. Firstly, the compensated readings of the sensors are used for building the polar histogram of the VFH algorithm. Secondly, a sensory fusion using the Kalman filter is proposed for the localization of the robot to compensate both the delay of the readings of an odometry sensor and the delay of the readings of the environmental sensors. The performance enhancements of the proposed obstacle avoidance algorithm from the viewpoint of efficient path generation and accurate goal positioning are also shown in this paper through some simulation experiments by the Marilou Robotics Studio Simulator.

• ETRI Journal
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• v.38 no.3
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• pp.551-559
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• 2016

In this paper, we propose a hybrid sensor calibration scheme for mobile crowdsensing applications. As the number of newly produced mobile devices containing embedded sensors continues to rise, the potential to use mobile devices as a sensor data source increases. However, because mobile device sensors are generally of a lower performance and cost than dedicated sensors, sensor calibration is crucial. To enable more accurate measurements of natural phenomena through the use of mobile device sensors, we propose a hybrid sensor calibration scheme for such sensors; the scheme makes use of mobile device sensors and existing sensing infrastructure, such as weather stations, to obtain dense data. Simulation results show that the proposed scheme supports low mean square errors. As a practical application of our proposed scheme, we built a temperature map of a city using six mobile phone sensors and six reference sensors. Thanks to the mobility of the sensors and the proposed scheme, our map presents more detailed information than infrastructure-based measurements.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.301-306
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• 2022

With the recent widespread implementation of Internet of Things (IoT) technology driven by Industry 4.0, self-powered sensors for wearable and implantable systems are increasingly gaining attention. Piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs), which convert biomechanical energy into electrical energy, can be considered as efficient self-powered sensor platforms. These are energy harvesters that are used as low-power energy sources. However, they can also be used as sensors when an output signal is used to sense any mechanical stimuli. For sensors, collecting high-quality data is important. However, the accuracy of sensing for practical applications is equally important. This paper provides a brief review of the performance advanced by the materials and structures of the latest PENG/TENG-based wearable sensors and intelligent applications applied using artificial intelligence (AI)

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.2
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• pp.93-100
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• 2007

The mobile robots to rescue a life in a disaster area and to explore planets demand high mobility as well as recognition of the environment. To avoid unknown obstacles exactly in unknown environment, accurate sensing is required. This paper proposes a sensor fusion to recognize unknown obstacles accurately by using low-cost sensors. Ultrasonic sensors and infrared sensors are used in this paper to avoid obstacles. If only one of these sensors is used alone, it is not useful fer the mobile robots to complete their tasks in the real world since the surrounding environment in the real world is complex and composed of many kinds of materials. So infrared sensor may not recognize transparent or reflective obstacles and ultrasonic sensor may not recognize narrow obstacles, far example, columns of small diameter. Therefore, I selected six ultrasonic sensors and five infrared sensors to detect obstacles. Then, I fused ultrasonic sensors with infrared sensors in order that both advantages and disadvantages of each sensor are utilized together. In fusing sensors, fuzzy algorithm is used to cope with the uncertainties of each sensor. TAMRY which is terrain-adaptive mobile robot is used as the mobile robot for experiments.

• Journal of the Society of Disaster Information
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• v.5 no.1
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• pp.122-139
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• 2009

USN Middleware plays roles of broker between sensors and applications. It collects sensor data, decides the situation and sends the result to the applications. It is not good to decide the situation from one sensor data, because it may error data or reflect small part of all. In this paper, we propose the disaster management model based on the concept 'group' and 'semantic information' from the sensing data. Group is the primary unit to decide the situation. It consists of several sensors which were installed in the same place and had the same pre-defined condition to act. For example, all fire sensors in the room simultaneously trigger the ring when the same pre-defined temperature is recorded. Then, the all fire sensors are included to the same one sensor group. All operations of the intelligent USN middleware are based on the 'group' unit. Disaster information is the result of the interpretation of the sensing data. based on the 'group', the disaster meaning is processed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.4
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• pp.344-365
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• 2009

The performance of a tracking system is greatly increased if multiple types of sensors are combined to achieve the objective of the tracking instead of relying on single type of sensor. To conduct the multi-modal tracking, we have previously developed a multi-modal sensor-based tracking model where acoustic sensors mainly track the objects and visual sensors compensate the tracking errors [1]. In this paper, we find a network synchronization problem appearing in the developed tracking system. The problem is caused by the different location and traffic characteristics of multi-modal sensors and non-synchronized arrival of the captured sensor data at a processing server. To effectively deliver the sensor data, we propose a time-based packet aggregation algorithm where the acoustic sensor data are aggregated based on the sampling time and sent to the server. The delivered acoustic sensor data is then compensated by visual images to correct the tracking errors and such a compensation process improves the tracking accuracy in ideal case. However, in real situations, the tracking improvement from visual compensation can be severely degraded due to the aforementioned network synchronization problem, the impact of which is analyzed by simulations in this paper. To resolve the network synchronization problem, we differentiate the service level of sensor traffic based on Weight Round Robin (WRR) scheduling at the routers. The weighting factor allocated to each queue is calculated by a proposed Delay-based Weight Allocation (DWA) algorithm. From the simulations, we show the traffic differentiation model can mitigate the non-synchronization of sensor data. Finally, we analyze expected traffic behaviors of the tracking system in terms of acoustic sampling interval and visual image size.

• Smart Structures and Systems
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• v.3 no.2
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• pp.133-146
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• 2007

This paper addresses a new type of broad and stage-based hybrid carbon fiber reinforced polymer (HCFRP) sensor that is suitable for the sensing of infrastructures. The HCFRP sensors, a type of composite sensor, are fabricated with three types of carbon tows of different strength and moduli. For all of the specimens, the active materials are carbon tows by virtue of their electrical conductivity and piezoresistivity. The measurement principles are based on the micro- and macro-fractures of different types of carbon tows. A series of experiments are carried out to investigate the sensing performances of the HCFRP sensors. The main variables include the stack order and volume fractions of different types of carbon tows. It is shown that the change in electrical resistance is in direct proportion to the strain/load in low strain ranges. However, the fractional change in electrical resistance (${\Delta}R/R_0$) is smaller than 2% prior to the macrofractures of carbon tows. In order to improve the resistance changes, measures are taken that can enhance the values of ${\Delta}R/R_0$ by more than 2 times during low strain ranges. In high strain ranges, the electrical resistance changes markedly with strain/load in a step-wise manner due to the gradual ruptures of different types of carbon tows at different strain amplitudes. The values of ${\Delta}R/R_0$ due to the fracture of high modulus carbon tows are larger than 36%. Thus, it is demonstrated that the HCFRP sensors have a broad and stage-based sensing capability.

• Journal of Sensor Science and Technology
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• v.22 no.3
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• pp.197-201
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• 2013

This paper describes the fabrication and characterization of graphene based carbon dioxide ($CO_2$) gas sensors. Graphene was synthesized by thermal decomposition of SiC. The resistivity $CO_2$ gas sensors were fabricated by pure graphene and graphene decorated Au nanoparticles (NPs). The Au NPs with size of 10 nm were decorated on graphene. Au electrode deposited on the graphene showed Ohmic contact and the sensors resistance changed following to various $CO_2$ concentrations. Resulting in resistance sensor using pure graphene can detect minimum of 100 ppm $CO_2$ concentration at $50^{\circ}C$, whereas Au/graphene can detect minimum 2 ppm $CO_2$ concentration at same at $50^{\circ}C$. Moreover, Au NPs catalyst improved the sensitivity of the graphene based $CO_2$ sensors. The responses of pure graphene and Au/graphene are 0.04% and 0.24%, respectively, at $50^{\circ}C$ with 500 ppm $CO_2$ concentration. The optimum working temperature of $CO_2$ sensors is at $75^{\circ}C$.

• Journal of Sensor Science and Technology
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• v.21 no.6
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• pp.425-428
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• 2012

This paper presents the fabrication and characterization of graphene based hydrogen sensors. Graphene was synthesized by annealing process of Ni/3C-SiC thin films. Graphene was transferred onto oxidized Si substrates for fabrication of chemiresistive type hydrogen sensors. Au electrode on the graphene shows ohmic contact and the resistance is changed with hydrogen concentration. Nanoparticle catalysts of Pd and Pt were decorated. Response factor and response (recovery) time of hydrogen sensors based on the graphene are improved with catalysts. The response factors of pure graphene, Pt and Pd doped graphenes are 0.28, 0.6 and 1.26, respectively, at 50 ppm hydrogen concentration.