MANAJEMEN SURAT MASUK DAN SURAT KELUAR DI UNIVERSITAS NEGERI SEMARANG MELALUI SIRADI (SISTEM INFORMASI SURAT DINAS)

The purpose of the study are (1) To know the management of incoming and outgoing mail at UNNES through SIRADI, (2) To know the obstacles faced in the management of incoming and outgoing mail at UNNES through SIRADI. The type of research used is descriptive qualitative research. Data collection techniques are literature research methods and field study methods. The conclusion in this study is (1) The management of incoming and outgoing mail has been managed electronically using SIRADI, (2) On the management of incoming mail, the letter is received centrally in the General Section of BUHK UNNES, (3) At the entry letter recording stage, there is still a work unit that requires an agenda book to record incoming mail, (4) At the stage of preparation of the concept of exit letter is done manually then the letter will be processed through SIRADI, (5) There are constraints such as network and human resources factor, there are still work units that do not digitize the letter i.e. by scanning and uploading mail files. Suggestions of this study are: (1) Need to improve the quality of the internet network to reduce network disturbances, thereby expediting the process of managing incoming and outgoing mail through SIRADI, (2) Need to have a unity of understanding between the staff at each work unit, (3) Writing in an agenda book or expedition book, so that when needed can look at the book to reduce input error.

Tracking of Stiffness Variation in Structural Members Using Input Error Function Observers

This study evaluates input error function observers for tracking of stiffness variation in real-time. The input error function is an Analytical Redundancy (AR)-based diagnosis method and necessitates a mathematical model of the system and system identification techniques. In practice, mathematical models used during numerical simulations differ from the actual status of the structure, and thus, accurate mathematical models are rarely available for reference. Noise is an unwanted signal in the input–output measurements but unavoidable in real-world applications (as in long span bridge trusses) and hard to imitate during numerical simulations. Simulation data from the truss system clearly indicates the effectiveness of the proposed structural damage detection method for estimating the severity of the damage. Optimization of the input error function can further automate the stiffness estimation in structural members and address critical aspects such as system uncertainties and the presence of noise in input–output measurements. Stiffness tracking in one of the planar truss members indicates the potential of optimization of the input error function for online structural health monitoring and implementing condition-based maintenance.

Sensitivity Analysis of Reliability of Low-Mobility Parallel Mechanisms Based on a Response Surface Method

Kinematic accuracy is a crucial indicator for evaluating the performance of mechanisms. Low-mobility parallel mechanisms are examples of parallel robots that have been successfully employed in many industrial fields. Previous studies analyzing the kinematic accuracy analysis of parallel mechanisms typically ignore the randomness of each component of input error, leading to imprecise conclusions. In this paper, we use homogeneous transforms to develop the inverse kinematics models of an improved Delta parallel mechanism. Based on the inverse kinematics and the first-order Taylor approximation, a model is presented considering errors from the kinematic parameters describing the mechanism’s geometry, clearance errors associated with revolute joints and driving errors associated with actuators. The response surface method is employed to build an explicit limit state function for describing position errors of the end-effector in the combined direction. As a result, a mathematical model of kinematic reliability of the improved Delta mechanism is derived considering the randomness of every input error component. And then, reliability sensitivity of the improved Delta parallel mechanism is analyzed, and the influences of the randomness of each input error component on the kinematic reliability of the mechanism are quantitatively calculated. The kinematic reliability and proposed sensitivity analysis provide a theoretical reference for the synthesis and optimum design of parallel mechanisms for kinematic accuracy.

Virtual Reality (VR)-Based Environmental Enrichment in Older Adults with Mild Cognitive Impairment (MCI) and Mild Dementia

Background: Despite an alarming rise in the global prevalence of dementia, the available modalities for improving cognition and mental wellbeing of dementia patients remain limited. Environmental enrichment is an experimental paradigm that has shown promising anti-depressive and memory-enhancing effects in pre-clinical studies. However, its clinical utility has remained limited due to the lack of effective implementation strategies. Objective: The primary objective of this study was to evaluate the usability (tolerability and interactivity) of a long-term virtual reality (VR)- based environmental enrichment training program in older adults with mild cognitive impairment (MCI) and mild dementia. A secondary objective was to assess the effect of VR-based environmental enrichment on stabilization of cognitive functioning and improvement of mental wellbeing in older adults with MCI and mild dementia. Methods: A total of seven participants (four patients with MCI and three with mild dementia) received biweekly VR-based environmental enrichment over a course of 6 months. The tolerability and interactivity of the participants in the VR training was serially assessed via virtual reality sickness questionnaire (VRSQ) and recording of input-error ratio. Cognitive functioning was assessed through Montreal cognitive assessment (MoCA) before and after the study. Mental wellbeing was assessed through Warwick-Edinburgh Mental Well Being Scale (WEMWBS). Results: VR-based environmental enrichment was well-tolerated by the patients with significant decrease in VRSQ scores (p < 0.01) and input-error ratio (p < 0.001) overtime. VR training was also effective in stabilization of MoCA scores over the course of therapy (non-significant difference in the MoCA scores before and after the therapy) and was associated with a trend (p < 0.1) towards improvement in WEMWBS scores between the first and the last assessments. Qualitative observations by the care-givers further corroborated a noticeable improvement in mental wellbeing of patients. Conclusions: This pilot study shows that VR can be a feasible, tolerable, and potentially effective tool in long-term support of older adults with MCI and mild dementia.

Evaluation of Trust Algorithm in a Mobile Ad-Hoc Network Using Artificial Engineering

A MANET network is made up of wireless nodes which operate without a defined structure, the arrangement of existing devices does not have any ministerial or control entity . the management tasks are carried out by the teams which are in the network, therefore a MANET network is said to be a managed network. As there is no infrastructure, such as, the bandwidth, availability, input error rate, among others, depend exclusively on the behaviour of the network users. This work presents a functional solution, which decreases the energy consumption in MANET networks, implemented using game theories and genetic algorithms solution. The evaluation of the pay-as-you-go strategy from the modified prisoner dilemma was shown to be an easy to implement and interpret tool for the network equipment. The processes of generation, combination and mutation of strategies demonstrated the in-centive of cooperation in the network, observed in the increase of the average confidence of the nodes, a very high value. The intervention of the protocol with the use of genetic algorithms, demonstrated ot be a form of implementation of solutions for problems of multiple agents, the increase in the evaluation of trust of the nodes was observed, which can be interpreted as a convergence of the nodes to cooperate. The results obtained in the simulations carried out show the functioning of the algorithm implemented and the functionality of the network after its implementation.

Event-triggered control for a linear continuous-time system under resource constraint environment

<div>A network-based event-triggered control is proposed for the continuous linear system. The primary feature of the proposed event-triggered control is to broadcast the current measurements when a pre-defined condition on input error is satisfied, which can avoid the redundant control updates from the controller to the actuator. Secondly, it provides an augmented system performance by appropriate selection of design parameters. Moreover, a dynamic threshold is introduced in the triggered condition to further improve the trade-off between resource utilization and system performance.</div>

Event-triggered control for a linear continuous-time system under resource constraint environment

<div>A network-based event-triggered control is proposed for the continuous linear system. The primary feature of the proposed event-triggered control is to broadcast the current measurements when a pre-defined condition on input error is satisfied, which can avoid the redundant control updates from the controller to the actuator. Secondly, it provides an augmented system performance by appropriate selection of design parameters. Moreover, a dynamic threshold is introduced in the triggered condition to further improve the trade-off between resource utilization and system performance.</div>

On the use of neural regulators

The application of neural networks for the synthesis of control systems is considered. Examples of synthesis of control systems using methods of reinforcement learning, in which the state vector is involved, are given. And the synthesis of a neural controller for objects with an inaccessible state vector is discussed: 1) a variant using a neural network with recurrent feedbacks; 2) a variant using the input error vector, where each error (except for the first one) enters the input of the neural network passing through the delay element. The disadvantages of the first method include the fact that for such a structure of a neural network it is not possible to apply existing learning methods with confirmation and for training it is required to use a data set obtained, for example, from a previously calculated linear controller. The structure of the neural network used in the second option allows the application of reinforcement learning methods, but the article provides a statement and its proof that for the synthesis of a control system for objects with three or more integrators, a neural network without recurrent connections cannot be used. The application of the above structures is given on examples of the synthesis of control systems for objects 1/s2 and 1/s3 presented in a discrete form.