Abstract: A soft Sensor is a model which is used to estimate the unmeasurable output of an industrial process. Designing a soft sensor is usually difficult because its modeling is often based on case data. These data commonly contain the outliers and noise as soft sensor design is been problem. In order to solve the problem and successfully design a soft sensor, this paper introduces a new approach for designing a robust soft sensor which is not affected by outliers especially batch outlier and long tail noise. To response this goal, a robust soft sensor based on Adaptive Neuro-Fuzzy Inference System (ANFIS) which is based on robust cost function such as the summation of the absolute cost function. To minimize the cost function the particle swarm optimization (PSO) algorithm was used. The subtractive clustering technique was used to determine the ANFIS structure. The proposed method for designing a soft sensor is implemented on a chemical plant and compared with soft sensor based on ANFIS which is based on quadratic cost function. The simulation result shows higher accuracy in prediction of output variable in new robust soft sensor.

Abstract: To measure the real value of velocity factor of the electromagnetic waves in the microwave microstrip transmission line was developed a modified resonant method and implements its measuring system. This system is using measuring the resonant frequency of a closed on the end one wave microstrip resonator. The method is designed to determine the velocity factor of the electromagnetic wave in the microstrip transmission line on substrates “policor” (95% Al₂O₃) for a small batch of microwave hybrid integrated circuits.

Abstract: This paper proposes a hazardous environment monitoring and control for monitoring information concerning safety and security, utilizing Wireless Sensor Network (WSN) technology. The proposed hazardous environment monitoring and control collects industrial environmental safety and security information from both inside and outside industry environment through WSN-based sensors, collects image information through vision system, and collects location information through wireless radio modules. This collected information is converted into a database through the Virtual Environment Monitoring Server consisting of a sensor manager, image information manager and wireless radio manager. The sensor manager manages information collected from the WSN sensors, the image information manager manages image information collected from vision system and the wireless radio manager processes the location information of the hazardous environment. In addition, a power supply based on solar cell and battery back-up is implemented with the central control unit so that it could also be used in environments with insufficient power infrastructure. Immediately after the occurrence of accident, the Accident Data Recorder (ADR) automatically logs the wireless sensors and vision system data for the legal verification and judicial purpose. This data can be used as a First Information Report (FIR) for accident damage investigation and estimation. And it could be expected that the usage of such a system could contribute to increasing safety and security.