Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network

A nuclear power plant (NPP) consists of an enormous number of components with complex interconnections. Various techniques to detect sensor errors have been developed to monitor the state of the sensors during normal NPP operation, but not for emergency situations. In an emergency situation with a reactor trip, all the plant parameters undergo drastic changes following the sudden decrease in core reactivity. In this paper, a machine learning model adopting a consistency index is suggested for sensor error detection during NPP emergency situations. The proposed consistency index refers to the soundness of the sensors based on their measurement accuracy. The application of consistency index labeling makes it possible to detect sensor error immediately and specify the particular sensor where the error occurred. From a compact nuclear simulator, selected plant parameters were extracted during typical emergency situations, and artificial sensor errors were injected into the raw data. The trained system successfully generated output that gave both sensor error states and error-free states

Joint Optimization for Secure and Reliable Communications in Finite Blocklength Regime

To realize ultra-reliable low latency communications with high spectral efficiency and security, we investigate a joint optimization problem for downlink communications with multiple users and eavesdroppers in the finite blocklength (FBL) regime. We formulate a multi-objective optimization problem to maximize a sum secrecy rate by developing a secure precoder and to minimize a maximum error probability and information leakage rate. The main challenges arise from the complicated multi-objective problem, non-tractable back-off factors from the FBL assumption, non-convexity and non-smoothness of the secrecy rate, and the intertwined optimization variables. To address these challenges, we adopt an alternating optimization approach by decomposing the problem into two phases: secure precoding design, and maximum error probability and information leakage rate minimization. In the first phase, we obtain a lower bound of the secrecy rate and derive a first-order Karush-Kuhn-Tucker (KKT) condition to identify local optimal solutions with respect to the precoders. Interpreting the condition as a generalized eigenvalue problem, we solve the problem by using a power iteration-based method. In the second phase, we adopt a weighted-sum approach and derive KKT conditions in terms of the error probabilities and leakage rates for given precoders. Simulations validate the proposed algorithm.Comment: 30 pages, 8 figure

Unified Modeling and Rate Coverage Analysis for Satellite-Terrestrial Integrated Networks: Coverage Extension or Data Offloading?

With the growing interest in satellite networks, satellite-terrestrial integrated networks (STINs) have gained significant attention because of their potential benefits. However, due to the lack of a tractable network model for the STIN architecture, analytical studies allowing one to investigate the performance of such networks are not yet available. In this work, we propose a unified network model that jointly captures satellite and terrestrial networks into one analytical framework. Our key idea is based on Poisson point processes distributed on concentric spheres, assigning a random height to each point as a mark. This allows one to consider each point as a source of desired signal or a source of interference while ensuring visibility to the typical user. Thanks to this model, we derive the probability of coverage of STINs as a function of major system parameters, chiefly path-loss exponent, satellites and terrestrial base stations' height distributions and density, transmit power and biasing factors. Leveraging the analysis, we concretely explore two benefits that STINs provide: i) coverage extension in remote rural areas and ii) data offloading in dense urban areas.Comment: submitted to IEEE journa

Analysis of interface management tasks in a digital main control room

Development of digital main control rooms (MCRs) has greatly changed operating environments by altering operator tasks, and thus the unique characteristics of digital MCRs should be considered in terms of human reliability analysis. Digital MCR tasks can be divided into primary tasks that directly supply control input to the plant equipment, and secondary tasks that include interface management conducted via soft controls (SCs). Operator performance regarding these secondary tasks must be evaluated since such tasks did not exist in previous analog systems. In this paper, we analyzed SC-related tasks based on simulation data, and classified the error modes of the SCs following analysis of all operational tasks. Then, we defined the factors to be considered in human reliability analysis methods regarding the SCs; such factors are mainly related to interface management and computerized operator support systems. As these support systems function to reduce the number of secondary tasks required for SC, we conducted an assessment to evaluate the efficiency of one such support system. The results of this study may facilitate the development of training programs as well as help to optimize interface design to better reflect the interface management task characteristics of digitalized MCRs

A Sensor Fault-Tolerant Accident Diagnosis System

Emergency situations in nuclear power plants are accompanied by an automatic reactor shutdown, which gives a big task burden to the plant operators under highly stressful conditions. Diagnosis of the occurred accident is an essential sequence for optimum mitigations; however, it is also a critical source of error because the results of accident identification determine the task flow connected to all subsequent tasks. To support accident identification in nuclear power plants, recurrent neural network (RNN)-based approaches have recently shown outstanding performances. Despite the achievements though, the robustness of RNN models is not promising because wrong inputs have been shown to degrade the performance of RNNs to a greater extent than other methods in some applications. In this research, an accident diagnosis system that is tolerant to sensor faults is developed based on an existing RNN model and tested with anticipated sensor errors. To find the optimum strategy to mitigate sensor error, Missforest, selected from among various imputation methods, and gated recurrent unit with decay (GRUD), developed for multivariate time series imputation based on the RNN model, are compared to examine the extent that they recover the diagnosis accuracies within a given threshold

Energy Efficiency Maximization Precoding for Quantized Massive MIMO Systems

The use of low-resolution digital-to-analog and analog-to-digital converters (DACs and ADCs) significantly benefits energy efficiency (EE) at the cost of high quantization noise for massive multiple-input multiple-output (MIMO) systems. This paper considers a precoding optimization problem for maximizing EE in quantized downlink massive MIMO systems. To this end, we jointly optimize an active antenna set, precoding vectors, and allocated power; yet acquiring such joint optimal solution is challenging. To resolve this challenge, we decompose the problem into precoding direction and power optimization problems. For precoding direction, we characterize the first-order optimality condition, which entails the effects of quantization distortion and antenna selection. We cast the derived condition as a functional eigenvalue problem, wherein finding the principal eigenvector attains the best local optimal point. To this end, we propose generalized power iteration based algorithm. To optimize precoding power for given precoding direction, we adopt a gradient descent algorithm for the EE maximization. Alternating these two methods, our algorithm identifies a joint solution of the active antenna set, the precoding direction, and allocated power. In simulations, the proposed methods provide considerable performance gains. Our results suggest that a few-bit DACs are sufficient for achieving high EE in massive MIMO systems

Development of Compact and High-efficient Scroll Compressor with Novel Bearing Structure

High-Side Shell(HSS) scroll compressors have been widely used for Variable Refrigerant Flow(VRF) system which is a powerful solution for the cooling and heating of commercial buildings. In order to improve the characteristics of the VRF system, a new HSS scroll compressor has been developed with a novel bearing structure. The core elements of the novel bearing structure are an outer-type bearing mounted on an orbiting scroll and a female-type eccentric journal inside of a shaft. The outer-type bush bearing which is made of engineering plastic without a back steel layer has been newly developed. The new HSS scroll compressor employing the novel bearing structure has a compact size, high efficiency, and low noise level compared to a conventional HSS scroll compressor. In order to confirm the advantages of the new HSS scroll compressor, basic tests and theoretical analysis have been performed in this study

Rate-Splitting Multiple Access for 6G Networks: Ten Promising Scenarios and Applications

In the upcoming 6G era, multiple access (MA) will play an essential role in achieving high throughput performances required in a wide range of wireless applications. Since MA and interference management are closely related issues, the conventional MA techniques are limited in that they cannot provide near-optimal performance in universal interference regimes. Recently, rate-splitting multiple access (RSMA) has been gaining much attention. RSMA splits an individual message into two parts: a common part, decodable by every user, and a private part, decodable only by the intended user. Each user first decodes the common message and then decodes its private message by applying successive interference cancellation (SIC). By doing so, RSMA not only embraces the existing MA techniques as special cases but also provides significant performance gains by efficiently mitigating inter-user interference in a broad range of interference regimes. In this article, we first present the theoretical foundation of RSMA. Subsequently, we put forth four key benefits of RSMA: spectral efficiency, robustness, scalability, and flexibility. Upon this, we describe how RSMA can enable ten promising scenarios and applications along with future research directions to pave the way for 6G.Comment: 17 pages, 6 figures, submitted to IEEE Network Magazin

Social Pressure-Induced Craving in Patients with Alcohol Dependence: Application of Virtual Reality to Coping Skill Training

OBJECTIVE: This study was conducted to assess the interaction between alcohol cues and social pressure in the induction of alcohol craving. METHODS: Fourteen male patients with alcohol dependence and 14 age-matched social drinkers completed a virtual reality coping skill training program composed of four blocks according to the presence of alcohol cues (x2) and social pressure (x2). Before and after each block, the craving levels were measured using a visual analogue scale. RESULTS: Patients with alcohol dependence reported extremely high levels of craving immediately upon exposure to a virtual environment with alcohol cues, regardless of social pressure. In contrast, the craving levels of social drinkers were influenced by social pressure from virtual avatars. CONCLUSION: Our findings imply that an alcohol cue-laden environment should interfere with the ability to use coping skills against social pressure in real-life situations.ope

Endoplasmic Reticulum Stress-Induced JNK Activation Is a Critical Event Leading to Mitochondria-Mediated Cell Death Caused by β-Lapachone Treatment

β-lapachone (β-lap) is a bioreductive agent that is activated by the two-electron reductase NAD(P)H quinone oxidoreductase 1 (NQO1). Although β-lap has been reported to induce apoptosis in various cancer types in an NQO1-dependent manner, the signaling pathways by which β-lap causes apoptosis are poorly understood.β-lap-induced apoptosis and related molecular signaling pathways in NQO1-negative and NQO1-overexpressing MDA-MB-231 cells were investigated. Pharmacological inhibitors or siRNAs against factors involved in β-lap-induced apoptosis were used to clarify the roles played by such factors in β-lap-activated apoptotic signaling pathways. β-lap leads to clonogenic cell death and apoptosis in an NQO1- dependent manner. Treatment of NQO1-overexpressing MDA-MB-231 cells with β-lap causes rapid disruption of mitochondrial membrane potential, nuclear translocation of AIF and Endo G from mitochondria, and subsequent caspase-independent apoptotic cell death. siRNAs targeting AIF and Endo G effectively attenuate β-lap-induced clonogenic and apoptotic cell death. Moreover, β-lap induces cleavage of Bax, which accumulates in mitochondria, coinciding with the observed changes in mitochondria membrane potential. Pretreatment with Salubrinal (Sal), an endoplasmic reticulum (ER) stress inhibitor, efficiently attenuates JNK activation caused by β-lap, and subsequent mitochondria-mediated cell death. In addition, β-lap-induced generation and mitochondrial translocation of cleaved Bax are efficiently blocked by JNK inhibition.Our results indicate that β-lap triggers induction of endoplasmic reticulum (ER) stress, thereby leading to JNK activation and mitochondria-mediated apoptosis. The signaling pathways that we revealed in this study may significantly contribute to an improvement of NQO1-directed tumor therapies