In order to achieve the goals of "carbon peak" and "carbon neutrality" and meet the needs of digital transformation of power load management, the optimization of the business environment has become ...more and more important. As an important supporting subsidiary system, the upgrading of customer service telephone recording technology is imminent. This article studies the key technologies of the distributed recording system for window customer service calls, focuses on the analysis of the cloud recording system architecture based on edge agent technology, and discusses the legal requirements for recording technology. Through the comparative analysis of traditional analog recording technology and modern digital recording technology, the concept of hierarchical recording of administrative telephone calls is proposed, and at the same time, the feasibility and necessity of large-scale use of encrypted calls in power grid enterprises are explored.
Electrostatic sensing is a novel technique for condition monitoring on tribological system. Two types of electrostatic sensors are currently available to detect the charge produced as a result of ...wear. The electrostatic sensors with different shapes and sizes have different features. This paper introduced the mathematical model and significant parameters for electrostatic sensors. By comparing and analyzing the simulation and experimental results, useful conclusions were investigated and listed for the development of sensor optimization and further electrostatic monitoring.
A Review on Electrostatic Monitoring Ruochen Liu; Hongfu Zuo; Jianzhong Sun ...
2017 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC),
2017-Aug.
Conference Proceeding
This paper reviews the technique of electrostatic monitoring. Over the past twenty years, the technique of electrostatic monitoring has been developed and applied to aircraft and engine condition ...monitoring in a wide range of components. The first successful application was to gas turbine engines where it was used to detect the presence of foreign debris in the gas path. Subsequent research has shown that electrostatic sensing can be a powerful non-contact technique to monitor a range of tribological contacts. The processes in the study of the technique are presented, which gives a bright outlook of its future prospects.
Scientific reasoning poses an excessive challenge for even the most advanced Large Language Models (LLMs). To make this task more practical and solvable for LLMs, we introduce a new task setting ...named tool-augmented scientific reasoning. This setting supplements LLMs with scalable toolsets, and shifts the focus from pursuing an omniscient problem solver to a proficient tool-user. To facilitate the research of such setting, we construct a tool-augmented training corpus named MathFunc which encompasses over 30,000 samples and roughly 6,000 tools. Building on MathFunc, we develop SciAgent to retrieve, understand and, if necessary, use tools for scientific problem solving. Additionally, we craft a benchmark, SciToolBench, spanning five scientific domains to evaluate LLMs' abilities with tool assistance. Extensive experiments on SciToolBench confirm the effectiveness of SciAgent. Notably, SciAgent-Mistral-7B surpasses other LLMs with the same size by more than 13% in absolute accuracy. Furthermore, SciAgent-DeepMath-7B shows much superior performance than ChatGPT.
With advancements in deep learning, artificial neural networks have been used increasingly in various document analysis problems such as character recognition, layout analysis, and orientation ...identification of documents. However, because of the ambiguity of the document image (caused by complicated appearances, multiple languages, etc.), it is difficult to use Convolutional Neural Networks (CNN) directly for orientation identification of the document. In order to solve this problem, we present offset neural networks (ONN), a new type of neural network that is especially designed for orientation identification. The ONN successfully reduces the negative influence of the ambiguous parts whose orientation cannot be distinguished. Meanwhile, the distinguishable parts of the document can be enhanced, which further improves the performance of the whole model. In the experiment, ONN shows better performance and robustness compared with the ordinary CNN. Especially for some extreme cases, the ONN is still able find the correct orientation. Considering that no one has ever proposed a dedicated neural network for orientation identification, our work is very practical and innovative.
Retrieval-based methods have been shown to be effective in NLP tasks via introducing external knowledge. However, the indexing and retrieving of large-scale corpora bring considerable computational ...cost. Surprisingly, we found that REtrieving from the traINing datA (REINA) only can lead to significant gains on multiple NLG and NLU tasks. We retrieve the labeled training instances most similar to the input text and then concatenate them with the input to feed into the model to generate the output. Experimental results show that this simple method can achieve significantly better performance on a variety of NLU and NLG tasks, including summarization, machine translation, language modeling, and question answering tasks. For instance, our proposed method achieved state-of-the-art results on XSum, BigPatent, and CommonsenseQA. Our code is released, https://github.com/microsoft/REINA .
In this paper, we proposed a revenue-based low-delay and efficient (R-LDE) algorithm for downlink packet scheduling in OFDMA systems. In actual operation of the network, telecommunication operators ...always expect the most revenue. Therefore, a simple revenue model is introduced and integrated into our proposed packet scheduling algorithm. The significances of R-LDE algorithm are threefold: firstly, it can guarantee the delay requirement of certain traffic; secondly, it can keep the efficiency of the system; thirdly, it considers the revenue of the telecommunication operators. The performance of R-LDE is evaluated in terms of packet delay, throughput and revenue with actual traffic models. It is then compared with conventional multi-carrier proportional fairness (MC-PF) algorithm and max SNR algorithm with mixed traffics such as FTP and video traffics. Simulation results show that the R-LDE algorithm can achieve the most scheduling revenue and performs much better than the algorithms stated before in terms of packet delay and throughput with video traffic at the cost of slight deterioration of FTP traffic performance.
Commonsense reasoning (CSR) requires the model to be equipped with general world knowledge. While CSR is a language-agnostic process, most comprehensive knowledge sources are in few popular ...languages, especially English. Thus, it remains unclear how to effectively conduct multilingual commonsense reasoning (XCSR) for various languages. In this work, we propose to utilize English knowledge sources via a translate-retrieve-translate (TRT) strategy. For multilingual commonsense questions and choices, we collect related knowledge via translation and retrieval from the knowledge sources. The retrieved knowledge is then translated into the target language and integrated into a pre-trained multilingual language model via visible knowledge attention. Then we utilize a diverse of 4 English knowledge sources to provide more comprehensive coverage of knowledge in different formats. Extensive results on the XCSR benchmark demonstrate that TRT with external knowledge can significantly improve multilingual commonsense reasoning in both zero-shot and translate-train settings, outperforming 3.3 and 3.6 points over the previous state-of-the-art on XCSR benchmark datasets (X-CSQA and X-CODAH).
Facial recognition is changing the way we live in and interact with our society. Here we discuss the two sides of facial recognition, summarizing potential risks and current concerns. We introduce ...current policies and regulations in different countries. Very importantly, we point out that the risks and concerns are not only from facial recognition, but also realistically very similar to other biometric recognition technology, including but not limited to gait recognition, iris recognition, fingerprint recognition, voice recognition, etc. To create a responsible future, we discuss possible technological moves and efforts that should be made to keep facial recognition (and biometric recognition in general) developing for social good.
With the continuous growth of the installed capacity of battery storage power stations and the expansion of single station scale, the operation and maintenance level has become the key to reducing ...costs, increasing efficiency, and improving safety level of energy storage power stations. Smart operation and maintenance based on big data analysis is an effective means. In order to solve the problems in big data analysis of maintenance of large-scale battery energy storage stations, an intelligent operation and maintenance platform has been designed and developed based on the management architecture of battery energy storage stations and safety zones in China. The data of 525MWh distributed battery energy storage station is transmitted, analyzed, and displayed on the platform. The results proved the effectiveness of the designed platform.