A new analytic approach for power system fault diagnosis employing the temporal information of alarm messages

Traditional analytic models for power system fault diagnosis are usually formulated as an unconstrained 0–1 integer programming problem. The key issue of the models is to seek the fault hypothesis that minimizes the discrepancy between the actual and the expected states of the concerned protective relays and circuit breakers. The temporal information of alarm messages has not been well utilized in these methods, and as a result, the diagnosis results may be not unique and hence indefinite, especially when complicated and multiple faults occur. In order to solve this problem, this paper presents a novel analytic model employing the temporal information of alarm messages along with the concept of related path. The temporal relationship among the actions of protective relays and circuit breakers, and the different protection configurations in a modern power system can be reasonably represented by the developed model, and therefore, the diagnosed results will be more definite under different circumstances of faults. Finally, an actual power system fault was served to verify the proposed method.

The effect of injury diagnosis on expected Postconcussion Syndrome (PCS) symptom reporting and illness perception

Objective: To determine if systematic variation of diagnostic terminology (i.e. concussion, minor head injury [MHI], mild traumatic brain injury [mTBI]) following a standardized injury description produced different expected symptoms and illness perceptions. We hypothesized that worse outcomes would be expected of mTBI, compared to other diagnoses, and that MHI would be perceived as worse than concussion. Method:108 volunteers were randomly allocated to conditions in which they read a vignette describing a motor vehicle accident-related mTBI followed by: a diagnosis of mTBI (n=27), MHI (n=24), concussion (n=31); or, no diagnosis (n=26). All groups rated: a) event ‘undesirability’; b) illness perception, and; c) expected Postconcussion Syndrome (PCS) and Posttraumatic Stress Disorder (PTSD) symptoms six months post injury. Results: On average, more PCS symptomatology was expected following mTBI compared to other diagnoses, but this difference was not statistically significant. There was a statistically significant group effect on undesirability (mTBI>concussion & MHI), PTSD symptomatology (mTBI & no diagnosis>concussion), and negative illness perception (mTBI & no diagnosis>concussion). Conclusion: In general, diagnostic terminology did not affect anticipated PCS symptoms six months post injury, but other outcomes were affected. Given that these diagnostic terms are used interchangeably, this study suggests that changing terminology can influence known contributors to poor mTBI outcome.

Monitoring smartphones for anomaly detection

In this paper we demonstrate how to monitor a smartphone running Symbian operating system and Windows Mobile in order to extract features for anomaly detection. These features are sent to a remote server because running a complex intrusion detection system on this kind of mobile device still is not feasible due to capability and hardware limitations. We give examples on how to compute relevant features and introduce the top ten applications used by mobile phone users based on a study in 2005. The usage of these applications is recorded by a monitoring client and visualized. Additionally, monitoring results of public and self-written malwares are shown. For improving monitoring client performance, Principal Component Analysis was applied which lead to a decrease of about 80 of the amount of monitored features.

Analyzing the effectiveness of graph metrics for anomaly detection in online social networks

Online social networks can be modelled as graphs; in this paper, we analyze the use of graph metrics for identifying users with anomalous relationships to other users. A framework is proposed for analyzing the effectiveness of various graph theoretic properties such as the number of neighbouring nodes and edges, betweenness centrality, and community cohesiveness in detecting anomalous users. Experimental results on real-world data collected from online social networks show that the majority of users typically have friends who are friends themselves, whereas anomalous users’ graphs typically do not follow this common rule. Empirical analysis also shows that the relationship between average betweenness centrality and edges identifies anomalies more accurately than other approaches.

Monitoring android for collaborative anomaly detection : a first architectural draft

Our daily lives become more and more dependent upon smartphones due to their increased capabilities. Smartphones are used in various ways, e.g. for payment systems or assisting the lives of elderly or disabled people. Security threats for these devices become more and more dangerous since there is still a lack of proper security tools for protection. Android emerges as an open smartphone platform which allows modification even on operating system level and where third-party developers first time have the opportunity to develop kernel-based low-level security tools. Android quickly gained its popularity among smartphone developers and even beyond since it bases on Java on top of "open" Linux in comparison to former proprietary platforms which have very restrictive SDKs and corresponding APIs. Symbian OS, holding the greatest market share among all smartphone OSs, was even closing critical APIs to common developers and introduced application certification. This was done since this OS was the main target for smartphone malwares in the past. In fact, more than 290 malwares designed for Symbian OS appeared from July 2004 to July 2008. Android, in turn, promises to be completely open source. Together with the Linux-based smartphone OS OpenMoko, open smartphone platforms may attract malware writers for creating malicious applications endangering the critical smartphone applications and owners privacy. Since signature-based approaches mainly detect known malwares, anomaly-based approaches can be a valuable addition to these systems. They base on mathematical algorithms processing data that describe the state of a certain device. For gaining this data, a monitoring client is needed that has to extract usable information (features) from the monitored system. Our approach follows a dual system for analyzing these features. On the one hand, functionality for on-device light-weight detection is provided. But since most algorithms are resource exhaustive, remote feature analysis is provided on the other hand. Having this dual system enables event-based detection that can react to the current detection need. In our ongoing research we aim to investigates the feasibility of light-weight on-device detection for certain occasions. On other occasions, whenever significant changes are detected on the device, the system can trigger remote detection with heavy-weight algorithms for better detection results. In the absence of the server respectively as a supplementary approach, we also consider a collaborative scenario. Here, mobile devices sharing a common objective are enabled by a collaboration module to share information, such as intrusion detection data and results. This is based on an ad-hoc network mode that can be provided by a WiFi or Bluetooth adapter nearly every smartphone possesses.

Solving inherent problems of anomaly detection by cooperation

Anomaly detection compensates shortcomings of signature-based detection such as protecting against Zero-Day exploits. However, Anomaly Detection can be resource-intensive and is plagued by a high false-positive rate. In this work, we address these problems by presenting a Cooperative Intrusion Detection approach for the AIS, the Artificial Immune System, as an example for an anomaly detection approach. In particular we show, how the cooperative approach reduces the false-positive rate of the detection and how the overall detection process can be organized to account for the resource constraints of the participating devices. Evaluations are carried out with the novel network simulation environment NeSSi as well as formally with an extension to the epidemic spread model SIR

Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm

Considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures, such as beams or plates. In the application on a complex structure, like steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates; then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution.

A study of post diagnosis breast cancer concerns for women living in rural and remote Queensland. Part I : Personal concerns

The findings presented in this paper are part of a research project designed to provide a preliminary indication of the support needs of postdiagnosis women with breast cancer in remote and isolated areas in Queensland. This discussion will present data that focuses on the women’s expressed personal concerns. For participants in this research a diagnosis of breast cancer involves a confrontation with their own mortality and the possibility of a reduced life span. This is a definite life crisis, creating shock and needing considerable adjustment. Along with these generic issues the participants also articulated significant issues in relation to their experience as women in a rural setting. These concerns centred around worries about how their partner and families cope during their absences for treatment, the additional burden on the family of having to cope with running the property or farm during the participant’s absence or illness, added financial strain brought about by the cost of travel for treatment, maintenance of properties during absences, and problems created by time off from properties or self-employment. These findings accord with other reports of health and welfare services for rural Australian and the generic literature on psycho-oncology studies of breast cancer.

Towards a reconceptualization of hope for patients with a diagnosis of cancer

Recent years have seen increased attention given to examining the phenomenon of hope in patients with metastatic cancer One of the results of this activity has been a greater appreciation of the significance of hope for the dying patient However, there are many questions about the experience of hope and its impact on the lives of patients with cancer which remain to be answered This paper discusses how hope is currently conceptualized in the nursing literature, and considers the implications that this conceptualization has for how we care for cancer patients Some alternative ways of looking at the experience and the impact of hope are also discussed

Monitoring of complex systems of interacting dynamic systems

Increases in functionality, power and intelligence of modern engineered systems led to complex systems with a large number of interconnected dynamic subsystems. In such machines, faults in one subsystem can cascade and affect the behavior of numerous other subsystems. This complicates the traditional fault monitoring procedures because of the need to train models of the faults that the monitoring system needs to detect and recognize. Unavoidable design defects, quality variations and different usage patterns make it infeasible to foresee all possible faults, resulting in limited diagnostic coverage that can only deal with previously anticipated and modeled failures. This leads to missed detections and costly blind swapping of acceptable components because of one’s inability to accurately isolate the source of previously unseen anomalies. To circumvent these difficulties, a new paradigm for diagnostic systems is proposed and discussed in this paper. Its feasibility is demonstrated through application examples in automotive engine diagnostics.

Acetylcholine receptor antibody in the diagnosis of myasthenia gravis

The acetylcholine receptor (AchR) antibody assay has a key role in the diagnosis of myasthenia gravis. In this article, the role of AchR antibody assay in the diagnosis of ocular and generalized myasthenia gravis is reviewed, and compared to standard means of diagnosing the disease by clinical and electrophysiological methods.

Acetylcholine receptor antibody in the diagnosis and management of myasthenia gravis

The relationship of acetylcholine receptor (AchR) antibodies to disease activity in myasthenia gravis (MG) is controversial. Some authors claim a direct correlation with disease activity and treatment, in particular plasmapheresis therapy, whereas others have commented on the poor overall correlation of antibody levels with clinical state. Antibody levels were examined in a population of MG patients and correlated with disease activity and response to treatment. Antibodies to skeletal muscle AchR were found in most patients with generalised MG (24/25) and in about half of the patients with purely ocular MG (6/10) and in neither of 2 patients with congenital MG. There was scant correlation with disease activity or response to treatment. It is concluded that the assay is more useful for diagnosis than for management of MG.

The effect of varied test instructions on neuropsychological performance following mild traumatic brain injury : an investigation of ‘diagnosis threat’

Diagnosis threat is a psychosocial factor that has been proposed to contribute to poor outcomes following mild traumatic brain injury (mTBI). This threat is thought to impair the cognitive test performance of individuals with mTBI because of negative injury stereotypes. University students (N= 45, 62.2% female) with a history of mTBI were randomly allocated to a diagnosis threat (DT, n=15), reduced threat (DT-reduced, n=15) or neutral (n=15) group. The reduced threat condition invoked a positive stereotype (i.e., that people with mTBI can perform well on cognitive tests). All participants were given neutral instructions before they completed baseline tests of: a) objective cognitive function across a number of domains; b) psychological symptoms; and, c) PCS symptoms, including self-reported cognitive and emotional difficulties. Participants then received either neutral, DT or DT-reduced instructions, before repeating the tests. Results were analyzed using separate mixed model ANOVAs; one for each dependent measure. The only significant result was for the 2 X 3 ANOVA on an objective test of attention/working memory, Digit Span, p<.05, such that the DT-reduced group performed better than the other groups, which were not different from each other. Although not consistent with predictions or earlier DT studies, the absence of group differences on most tests fits with several recent DT findings. The results of this study suggest that it is timely to reconsider the role of DT as a unique contributor to poor mTBI outcome.

Identification of radiological alveolar pneumonia in children with high rates of hospitalized respiratory infections : Comparison of WHO-defined and pediatric pulmonologist diagnosis in the clinical context

Background A reliable standardized diagnosis of pneumonia in children has long been difficult to achieve. Clinical and radiological criteria have been developed by the World Health Organization (WHO), however, their generalizability to different populations is uncertain. We evaluated WHO defined chest radiograph (CXRs) confirmed alveolar pneumonia in the clinical context in Central Australian Aboriginal children, a high risk population, hospitalized with acute lower respiratory illness (ALRI). Methods CXRs in children (aged 1-60 months) hospitalized and treated with intravenous antibiotics for ALRI and enrolled in a randomized controlled trial (RCT) of Vitamin A/Zinc supplementation were matched with data collected during a population-based study of WHO-defined primary endpoint pneumonia (WHO-EPC). These CXRs were reread by a pediatric pulmonologist (PP) and classified as pneumonia-PP when alveolar changes were present. Sensitivities, specificities, positive and negative predictive values (PPV, NPV) for clinical presentations were compared between WHO-EPC and pneumonia-PP. Results Of the 147 episodes of hospitalized ALRI, WHO-EPC was significantly less commonly diagnosed in 40 (27.2%) compared to pneumonia-PP (difference 20.4%, 95% CI 9.6-31.2, P < 0.001). Clinical signs on admission were poor predictors for both pneumonia-PP and WHO-EPC; the sensitivities of clinical signs ranged from a high of 45% for tachypnea to 5% for fever + tachypnea + chest-indrawing. The PPV range was 40-20%, respectively. Higher PPVs were observed against the pediatric pulmonologist's diagnosis compared to WHO-EPC. Conclusions WHO-EPC underestimates alveolar consolidation in a clinical context. Its use in clinical practice or in research designed to inform clinical management in this population should be avoided. Pediatr Pulmonol. 2012; 47:386-392. (C) 2011 Wiley Periodicals, Inc.