In this paper, a new alternative method for order reduction of high order systems is presented based on optimization of multi objective fitness function by using Harmony Search algorithm. At first, step response of full order system is obtained as a vector, then, a suitable fixed structure considered for model order reduction which order of original system is bigger than fixed structure model. Parameters of model order reduction are determined by minimizing the multi objective fitness function where the fitness function is conducted with integral square error and H¥ norm of error.  The harmony search algorithm is applied for minimizing the multi objective fitness function. To assure the stability, the Routh criterion is applied for specifying the stability conditions. This stability condition is considered as constraints in optimization problem. To present the accuracy of the proposed method, three test systems are reduced. The obtained results show that the proposed approach performs very well.

Background and Objective: Incidental events due to human error in the maintenance activities of molecular sieve replacement in the dehydration unit of gas refineries indicate the inadequate effectiveness of the techniques available to identify human errors. This study aimed to identify and analyze human errors in this maintenance activity using the predictive human error analysis (PHEA) technique and provide appropriate control strategies to prevent or reduce the consequences of identified errors. Materials and Methods: In this study, all activities and tasks performed to achieve the main goals were identified using hierarchical task analysis (HTA) technique. Subsequently, possible types of human errors in each phase of the above processes were predicted and identified using the PHEA method and a series of guidance keywords. After determining the consequences of each error, appropriate control methods were also provided. Results: Out of 11 operations and 111 sub task occupations analyzed in the PHEA worksheets, a total of 337 errors were identified, of which 246, 13, 28, 33, and 17cases were regarded as action, checking, retrieval, information transfering and planning errors, respectively. The most significant identified errors were forgetting the task, doing the task incorrectly, incompletely, at the wrong time, making the right decision on the wrong option, and doing the task more or less than necessary. Conclusion: According to the obtained results, it can be concluded that functional errors were the most prevalent type of errors. Theses results emphasize the necessity of using control strategies in terms of adequate and effective training for personnel, the preparation and modification of instructions, the monitoring of implementation of work and hardware changes.

Risk assessment processes refer to a set of actions aimed at identifying, analyzing, and managing risks in various workplaces. These processes are a proactive approach to controlling workplace hazards. In fact, risk assessment forms the basis for decision-making in developing occupational safety and health strategies. This can lead to the promotion of a safety culture, employee participation in safety and health programs, and ultimately, the prevention and reduction of occupational accidents (1, 2). Hospitals are among the most challenging work environments, exposing employees to a wide range of hazards that can lead to numerous occupational accidents. Particularly notable are accidents resulting from human error in these dynamic settings (2). Risk assessment in hospitals is critically important and plays a key role in improving the quality of healthcare services, ensuring the safety of patients and staff, and managing resources. This process helps to identify and analyze potential hazards that may expose patients to harm. By identifying these risks, hospitals can implement preventive measures to mitigate them. Additionally, hospital staff are exposed to various hazards. Risk assessment helps identify these hazards and can lead to the design of a safe working environment and improved working conditions for employees. Moreover, by identifying and managing risks, hospitals can enhance the quality of services provided to patients. Risk assessment also assists in identifying weaknesses in compliance with regulations and can help hospitals maintain necessary licenses and accreditation (3).

INTRODUCTIONExtraction and processing of various features with the help of aerial imagery reduces the time and financial costs associated with the use of ground mapping and the resulting human error. Advances in the field of aerial sensors in terms of spatial and spectral resolution with precise place and performance picking up altitude from the ground have led to the use of each part of information about terrestrial phenomena such as spectral and spatial characteristics Brought. Today, complementary data used to detect complications are Lidar data, the sensor of which is sent and received, and the electromagnetic spectrum in the near-infrared spectrum (in its aerial form) and joined the spectrum. Pays close infrared and green band (in space type). DATA AND METHODSLidar data and spectral images were analyzed using different types of algorithms effective in landfill extraction to assess density. New layers of images were obtained in the form of raster from the study area, which was analyzed by performing slope extraction steps on flat and sloping surfaces. Buildings that were definitely not buildings were removed. The size and spectral characteristics of the missing structures were identified and the parcels were redistributed to extract the impermeable surfaces. Which led to the achievement of two levels of parcels and impenetrable points. The data set is related to the northern part of Bandar Anzali, which includes a vertical aerial photograph, irregular cloud points of the region with dense one to two points per square meter with an average point space of 0.69 square meters, and vertical aerial photograph with spatial resolution. It is 8 cm square. RESULTS AND DISCUSSIONIn this study, a different method for extracting buildings using airborne Lidar data and ultracam images was presented. The proposed system used geometric and spatial information of Lidar data and ultracam images, which included three general steps, in the first step; Lidar data were filtered and extracted using spectral clustering of buildings. In the second step; The obtained model was compared with the two-dimensional boundaries of buildings by the height threshold method. In the third step; After extraction, the first building boundaries were merged with the structures extracted by the checker algorithm. In the stage of separating terrestrial from non-terrestrial points, all points related to land were classified and extracted. The remaining points were classified as roof points, which were dealt with in the fault section of the buildings. All the functions used enabled the system to successfully extract the structures from the Lidar data. CONCLUSIONThe data for the first return points were subtracted from the data for the last return points and a fixed value was obtained which depended on the altitude accuracy of the difference between the two returns. In addition to the mentioned method, the clustering method was used during the research that each cluster belonged to a roof section so that the characteristics of each surface model could be easily determined.Then, to complete the shape of the roof, the footprint of the building that was extracted was used. In fact, the borderlines and inner vertices extracted only part of the shape of the border. Other sections, such as vertical edges, were not detected due to intersection. This is due to the lack of front sampling. Finally, the items extracted through spectral clustering in eCoginition software and two-dimensional boundaries extracted from ENVI Lidar software, to increase the accuracy of land surface extraction (buildings) and make the area of ​​buildings studied in this data Were merged. As mentioned; After extraction, the primary building boundaries were merged with the structures extracted by the checker algorithm. In the section of buildings diagnostics, buildings with errors were discussed and the evaluation of the results showed that the system used has relatively reached the set goals and the methods used include the threshold method. Elevation, clustering, spectral method, and integration method were evaluated for each of the four blocks with error rates of 28%, 15%, and 0%, respectively, based on the area of ​​extracted tolls to the study area. The error of each building was first examined in general and then in detail, and it was found that aerial Lidar technology has an extraordinary ability to collect very right and dense samples of altitude measurements of cities and a new level of detail information can be Accurately extracted building density automatically and efficiently from aerial Lidar data. In 417 buildings that were surveyed and analyzed, the height of the buildings was achieved with high accuracy and all the buildings in the study area were extracted with a compact and organic density as well as scattered and planned.