This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

Organisationspädagogik beschäftigt sich mit der Frage nach den Programmatiken und Semantiken des Organisierens, die für organisationale Strategien und Praktiken relevant werden. Sie beschäftigt sich mit der Frage nach der Veränderung organisationaler Routinen und den Akteuren des Wandels. Sie untersucht die Grammatiken, Regime und organisationalen Ordnungen des Neuen und beobachtet die Genese des Neuen im Organisieren und in der Forschung. Sie konzeptualisiert das Neue im Prozess des Organisierens. Wie also lassen sich aktuelle Programmatiken und Semantiken sowie Wissensordnungen des Neuen im Organisieren differenziert rekonstruieren? Welche Wissensbestände des Neuen werden wirksam für organisationale Programme, Strategien und Vorgehensweisen? Diesen Fragen geht der vorliegende Beitrag mit Blick auf die Wissenskulturen und -kontexte offener Gestaltungs-, Entwicklungsund Innovationsprozesse nach. Im internationalen Diskurs zeigt sich deutlich ein Trend hin zum Begriff des „Design“ als Reformulierung von Gestaltungsstrategien. So wird „Design” und besonders „Design thinking” zu einem „rising star” am globalen Firmament organisationalen Wandels und der Innovation (Weber 2013). Wie im Folgenden deutlich werden wird, formiert sich die Rede von der „Gestaltung“ sowohl in wissenschaftlichen wie auch in praxisbezogenen Diskursen. Als organisationales Veränderungswissen für Multistakeholderund „Open Innovation“Prozesse wird „Design“ und „Design-Thinking“ weit über seine klassische Gegenstandsbereiche hinaus Geltung zugesprochen und der Anspruch auf Definitionsmacht formuliert. „Design“ und „Design Thinking“ wird ein methodischer, organisationaler und sogar paradigmatischer Status zugewiesen (Mareis 2011). Dieser Begriff läßt sich als neue Mode des Organisierens und „Managens“ von organisationaler Veränderung diskutieren (Jackson 2001; Weber 2005). Handelt es sich hierbei lediglich um eine Management-Mode am Beratungsmarkt, einen Mythos des Organisierens oder ein neues Machtmodell des Organisierens (Weber 2007), das die Denkund Argumentationsfigur der „lernenden Organisation“ ablöst? Im Anschluss an eine wissensbasierte Perspektive auf den Designdiskurs (Weber

Change by design.

With the popularity of complex hydrologic models, the time taken to run these models is increasing substantially. Comparing and evaluating the efficacy of different optimization algorithms for calibrating computationally intensive hydrologic models is becoming a nontrivial issue. In this study, five global optimization algorithms (genetic algorithms, shuffled complex evolution, particle swarm optimization, differential evolution, and artificial immune system) were tested for automatic parameter calibration of a complex hydrologic model, Soil and Water Assessment Tool (SWAT), in four watersheds. The results show that genetic algorithms (GA) outperform the other four algorithms given model evaluation numbers larger than 2000, while particle swarm optimization (PSO) can obtain better parameter solutions than other algorithms given fewer number of model runs (less than 2000). Given limited computational time, the PSO algorithm is preferred, while GA should be chosen given plenty of computational resources. When applying GA and PSO for parameter optimization of SWAT, small population size should be chosen. Copyright © 2008 John Wiley & Sons, Ltd.

https://swat.tamu.edu/media/90107/hp-paper.pdf

Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model

We are delighted to have the opportunity to talk with Tony about how his work touches on issues of imitation and contagion—a loaded term unpacked within his 2013 book.

An Interview with Tony David Sampson: Author of Virality: Contagion Theory in the Age of Networks

Increasing use of renewable energy sources, liberalized energy markets and most importantly, the integrations of various monitoring, measuring and communication infrastructures into modern power system network offer the opportunity to build a resilient and efficient grid However, it also brings about various threats of instabilities and security concerns in form of cyberattack, voltage instability, power quality (PQ) disturbance among others to the complex network The need for efficient methodologies for quicker identification and detection of these problems have always been a priority to energy stakeholders over the years In recent times, machine learning techniques (MLTs) have proven to be effective in numerous applications including power system studies In the literature, various MLTs such as artificial neural networks (ANN), Decision Tree (DT), support vector machines (SVM) have been proposed, resulting in effective decision making and control actions in the secured and stable operations of the power system Given this growing trend, this paper presents a comprehensive review on the most recent studies whereby MLTs were developed for power system security and stability especially in cyberattack detections, PQ disturbance studies and dynamic security assessment studies The aim is to highlight the methodologies, achievements and more importantly the limitations in the classifier(s) design, dataset and test systems employed in the reviewed publications A brief review of reinforcement learning (RL) and deep reinforcement learning (DRL) approaches to transient stability assessment is also presented Finally, we highlighted some challenges and directions for future studies

/pdf/a-review-of-machine-learning-approaches-to-power-system-4q2b9d8uxr.pdf

A Review of Machine Learning Approaches to Power System Security and Stability

This paper presents an Artificial Immune-based optimization technique for solving the economic dispatch problem in a power system. The main role of electrical power utility is to ensure that electrical energy requirement from the customer is served. However in doing so, the power utility has also to ensure that the electrical power is generated with minimum cost. Hence, for economic operation of the system, the total demand must be appropriately shared among the generating units with an objective to minimize the total generation cost for the system. Economic Dispatch is a procedure to determine the electrical power to be generated by the committed generating units in a power system so that the total generation cost of the system is minimized, while satisfying the load demand simultaneously. The proposed technique implemented Clonal Selection algorithm with several cloning, mutation and selection approaches. These approaches were tested and compared in order to determine the best strategy for solving the economic dispatch problem. The feasibility of the proposed techniques was demonstrated on a system with 18 generating units at various loading conditions. The results show that Artificial Immune System optimization technique that employed adaptive cloning, selective mutation and pair-wise tournament selection has provided the best result in terms of cost minimization and least execution time. A comparative study with λ-iteration optimization method and Genetic Algorithm was also presented.

Artificial immune-based optimization technique for solving economic dispatch in power system

Economic dispatch is one of the important issues in power system operation and planning. Economic dispatch requires reliable technique to achieve minimal cost; otherwise, a non-optimal solution may cause non-economic electricity generation to the utility. Single area economic dispatch does not make complete electricity generation of the whole system on the electrical transmission network. Thus, multi-area economic dispatch implementation leads to complete consideration for power transmission system. This paper presents multi-area economic dispatch performance using swarm intelligence technique. In this study, swarm intelligence technique, namely the particle swarm optimization technique (PSO) is employed for solving multi-area economic dispatch problems. The algorithm is tested on a 2-area 48-bus power system with different case studies. Variation in active power loading in achieving an optimal solution is also considered in this study. Several trials were taken into consideration to assess the consistency of results. Comparative studies were performed with respect to evolutionary programming (EP) and revealed that PSO yields better results as compared to the EP.

/pdf/multi-area-economic-dispatch-performance-using-swarm-2u4hh062y1.pdf

Multi-Area Economic Dispatch Performance Using Swarm Intelligence Technique Considering Voltage Stability

In this paper, an implementation of vehicle ventilation system using microcontroller NodeMCU is described, as an internet of things (IoT) platform. A low-cost wireless fidelity (Wi-Fi) microchip ESP8266 integrated with NodeMCU provides full-stack transmission control protocol/internet protocol (TCP/IP) to communicate between mobile applications. This chip is capable to monitor and control sensor devices connected to the IoT platform. In this reserach, data was collected from a temperature sensor integrated to the platform, which then monitored using Blynk application. The vehicle ventilation system was activated/deactivated through mobile application and controlled using ON/OFF commands sent to the connected devices. From the results, the vehicle ventilation system built using NodeMCU microcontroller is capable to provide near real-time data monitoring for temperature in the car before and after the ventilation system was applied.

https://www.beei.org/index.php/EEI/article/download/2669/1757

Implementation of vehicle ventilation system using NodeMCU ESP8266 for remote monitoring

This project focuses on face recognition implementation in creating fully automated attendance system with a cloud. Cloud services will provide a useful information regarding the attendance such as attendance summary performance and visualizing the data into graph and chart. In this study, we aim to create an online student attendance database, interfaced with a face recognition system based on raspberry pi 3 model B. A graphical user interface (GUI) will provide ease of use for data analysis on the attendance system. This work used open computer vision library and python for face recognition system combined with SFTP to establish connection to an internet server which runs on PHP and Node.js. The results showed that by interfacing a face recognition system with a server, a real-time attendance system can be built and be monitored remotely.  

/pdf/real-time-internet-based-attendance-using-face-recognition-34oizddoox.pdf

Real-time internet based attendance using face recognition system

As the world is progressing forward, the load demand in the power system has been continuously increasing day by day. This situation has forced the power system to operate under stress condition due to its limitation. Therefore, due to the stressed condition, the transmission losses faced higher increment with a lower minimum voltage. Theoretically, the installation of the Flexible AC Transmission System (FACTS) device can solve the problem experienced by the power system. This paper presents the Whale Optimization Algorithm for loss minimization using FACTS devices in the transmission system. Thyristor controlled series compensator (TCSC) is chosen for this study. In this study, WOA is developed to identify the optimal sizing of FACTS device for loss minimization in the power system. IEEE 30-bus RTS was used as the test system to validate the effectiveness of the proposed algorithm.

https://beei.org/index.php/EEI/article/download/1442/1850

Saiful Izwan Suliman

Papers

Artificial immune-based optimization technique for solving economic dispatch in power system

Multi-Area Economic Dispatch Performance Using Swarm Intelligence Technique Considering Voltage Stability

Implementation of vehicle ventilation system using NodeMCU ESP8266 for remote monitoring

Real-time internet based attendance using face recognition system

FACTS device installation in transmission system using whale optimization algorithm