False data injection attacks have recently been introduced as an important class of cyber attacks against smart grid's wide area measurement and monitoring systems. These attacks aim to compromise the readings of multiple power grid sensors and phasor measurement units in order to mislead the operation and control centers. Recent studies have shown that if an adversary has complete knowledge on the power grid topology and transmission-line admittance values, he can adjust the false data injection attack vector such that the attack remains undetected and successfully passes the residue-based bad data detection tests that are commonly used in power system state estimation. However, in this paper, we explain that a realistic false data injection attack is essentially an attack with incomplete information due to the attackers lack of real-time knowledge with respect to various grid parameters and attributes such as the position of circuit breaker switches and transformer tap changers and also because of the attacker's limited physical access to most grid facilities. We mathematically characterize false data injection attacks with incomplete information from both the attacker's and grid operator's viewpoints. Furthermore, we introduce a novel vulnerability measure that can compare and rank different power grid topologies against such attacks. To the best of our knowledge, this paper is the first study to investigate false data injection attacks with line admittance uncertainty.

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Abstract. This paper presents a parallel genetic simulated annealing (PGSA) algorithm that has been developed and applied to optimize continuous problems. In PGSA, the entire population is divided into subpopulations, and in each subpopulation the algorithm uses the local search ability of simulated annealing after crossover and mutation. The best individuals of each subpopulation are migrated to neighboring ones after certain number of epochs. An implementation of the algorithm is discussed and the performance evaluation is made against a standard set of test functions. PGSA shows some remarkable improvement in comparison with the conventional simulated annealing, parallel genetic algorithm.

The main objective of this study is to show a method for calculating entropy generation (Sgen) in a human body under various environmental and physiological conditions. The Sgen in a human body is a measure of activeness of motions, reactions, and irreversibility of processes occurring in a body and is a kind of holistic and thermodynamic index, which characterizes a human body as a whole. Human body at healthier and normal condition generates the least amount of Sgen. Heat transfer over a human body, activity (at rest, Sgen = 0.21J/sK or exercise, Sgen=2.19 J/sK or at death, Sgen = 0J/sK), ambient, body and mean radiant temperatures, emissivity and absorbity of human skin, internal heat elimination, body weight and height, and air speed effect much more on the Sgen in a human body compared to the effects of mass exchange into and out of the body, internal heat production, cross-sectional area of human body, clothing, altitude, and relative humidity of the surrounding air. Among these factors entropy production due to heat transfer over a human body plays a significant role in the total entropy generation rate. .

The selection of optimal machining parameters plays an important part in computer-aided manufacturing. The optimisation of machining parameters is still the subject of many studies. Genetic algorithm (GA) and simulated annealing (SA) have been applied to many difficult combinatorial optimisation problems with certain strengths and weaknesses. In this paper, genetic simulated annealing (GSA), which is a hybrid of GA and SA, is used to determine optimal machining parameters for milling operations. For comparison, basic GA is also chosen as another optimisation method. An application example that has previously been solved using geometric programming (GP) method is presented. The results indicate that GSA is more efficient than GA and GP in the application of optimisation.