Analysis on Complaint Behavior of Electric Power Customers

Online Road Complaints Registration System

Analysis on Complaint Behavior of Electric Power Customers

The data of electric power customer complaints from EZ power supply company in Hubei Province from January 2015 to May 2018 is obtained. During this time period, EZ power supply company received a total of 622 complaints from 95598 hotline. The annual distribution of complaints is shown in Table I. The highest number of complaints was in 2016, a total of 238, which fell sharply in 2017, but rebounded in 2018. There were 75 complaints from January to May 2018, with an increase of 62 cases compared with the number of complaints in the same period in 2017. The complaints were mainly concentrated in the rural network, a total of 507 cases, accounting for 81.51%. There were 115 cases in the urban network, accounting for 18.49%. Code Shoppy

According to “The handling regulation of power supply service complaints” from SGCC, electric power customer complaints can be divided into five types, including power supply quality complaints, business complaints, service complaints, power grid construction complaints and power outage and supply complaints. Power supply quality complaints refer to the complaints caused by long-term power supply quality problems, which affects customer normal life and production, such as voltage deviation, frequency deviation, voltage imbalance and voltage fluctuation or flicker. Business complaints refer to the complaints caused by working timeout, negligence and error happened in the process of handling the specific business operation. Service complaints mean the complaints caused by irregular service behavior of power supply company employees, inadequate and inconvenient service channels of power supply company. Power grid construction complaints imply the complaints happened during the process of power grid construction, such as incomplete transformation of power supply facilities and irregular power construction. Power outage and supply complaints refer to the complaints caused by service errors in the process of power outage, supply management and on-site emergency repair services. Table II shows the classification and proportion of 622 complaints from EZ power supply company. Among these 622 complaints, the number of power supply quality complaints was the maximum, 241 cases, accounting for 38.75%; followed by 151 business complaints, accounting for 24.28%.
The number and proportion of customer complaints from 2015 to 2018 are shown in Fig. 1. The proportion of power supply quality complaints and power grid construction complaints increased at first and then decreased, which indicated that the effect of power network reform in the past three years was remarkable. The proportion of business complaints and power outage and supply complaints decreased at first and then increased.
There are 24 power supply stations in this regional power grid. Each power supply station is responsible for one town. The population of each town is different. Fig. 9 shows the total number of complaints and the number of complaints per 10,000 households in each power supply station. HH power supply station has the largest number of complaints among these 24 power supply stations, with a total of 64 complaints. The biggest amount of complaints per 10,000 households is YY power supply station, with 28.43 cases per 10,000 households.
From 2015 to 2017, complaints from seven power supply stations, such as HH power supply station, have declined year by year. There is no complaint from CG and JK power supply stations since 2017, and no complaint from DS power supply station since 2016. However, complaints from TH power supply station increase year by year. From January to May 2018, the total number of complaints from 19 power supply stations increase year-on-year. The complaints from GK power supply station have the maximum increase, with an increase of nine. There are 20 power supply stations whose power supply quality complaints have the largest proportion of the total number of their complaints. Fig. 10 shows the distribution of power supply quality complaints in power supply stations. GK power supply station has the largest number of complaints about power supply quality, with 32 cases. Four power supply stations have no power supply quality complaint for three consecutive years. The number of power supply quality complaints per 10,000 households in YY power supply station is the maximum, 16.72 cases per 10,000 households.
Fig. 11 shows the distribution of business complaints from power supply stations. HH power supply station has the largest number of business complaints, with 21 cases. Three power supply stations have no business complaint for three consecutive years. The number of business complaints per 10,000 households in YY power supply station is the maximum, 8.36 cases per 10,000 households.
Fig. 12 shows the distribution of service complaints from power supply stations. HH power supply station has the largest number of service complaints, with 10 cases. Four power supply stations have no service complaint for three consecutive years. The number of service complaints per 10,000 households in DD power supply station is the maximum, 4.01 cases per 10,000 households.
Fig. 13 shows the distribution of power grid construction complaints from power supply stations. TZH and ZS power supply stations have the largest number of power grid construction complaints, with 6 cases. Three power supply stations have no service complaint for three consecutive years. The number of power grid construction complaints per 10,000 households in TZ power supply station is the maximum, 5.63 cases per 10,000 households.
Fig. 14 shows the distribution of power outage and supply complaints from power supply stations. GD and HH power supply stations have the largest number of power outage and supply construction complaints, with 8 cases. Eight power supply stations have no power outage and supply complaint for three consecutive years. The number of power outage and supply complaints per 10,000 households in TH power supply station is the maximum, 3.27 cases per 10,000 households. Click Here

Group Signature Entanglement in E-voting System

Online Voting System Project Application

Group Signature Entanglement in E-voting System

Xuet al. [4] proposed a scheme which supports anonymity of the voters in the e-voting system by applying the concept of blinding and grouping signature. This scheme seems to be easier than the other quantum signature schemes because i t d o e s not involve entanglement. In t h e e-voting system, the message has to be signed by the manager of the office. However, the content of the message d o e s not h a v e to be readable by any person other than the owner of the message (blind signature scheme). Also, Xu’s paper uses the grouping signature to provide anonymity of voters in the e-voting system, whereas the voter information, such as location information, has to be secure and non-readable by any person. Some e-voting systems could be applied in different branches and offices in different locations, so signing the message from a specific manager might reveal the location information of the voter. Thus, by applying grouping signature with different managers on the same message, f tracing the sender could be eliminated. However, the verifier cannot know the identity of the signer; he/she can only verify the validity of the signature. This paper is different than some other schemes that propose different services. Xuet al. [4] proposes a blind signature scheme using a group signature scheme for a distributed e-voting system without using the entangled state concept, and this scheme can represent a high level of efficiency. The authors explained some disadvantages, such as using a symmetric scheme. Also, the inspector in this scheme is the only person who can verify the message which makes the scheme elastic with only e-voting systems. In [10], the authors proposed a new quantum protocol that provides anonymous voting with anonymity check. This protocol has two main characteristics. First, the value of a voter’s vote is unknown to other voters and the tallyman. Second, a non-exaggeration t e c h n i q u e h a s b e e n i m p l e m e n t e d to prevent malicious voters from voting twice. Each voter makes a binary decision (0,1); 0 means no and 1 means yes. There is a tallyman who collects the ballots and announces the results. The main idea, after the voting process, is that the ballots arereturned to voters again to allow for two voters to check the anonymity of the vote counting process bypreparing an entangled state of two ballots. Thus, any attempt by a curious tallyman to g a i n information about voting results leads to th e d e st r uc tio n of the entanglement, which can be detected by the voters. The entangled state is generated using one of four Bell bases to create a Bell state as follows: The four Bell bases are:√√√√The voters carry out the ballot test: – The voters who have chosen to vote measure their qubits in computational basis. If there is a difference from the sent ballot, they state the ballot test failure. – On the other hand, the voters who have chosen to check the anonymity make the measurement of their qubits in the Bell basis. If there is a difference from the Bell state, they state the ballot test failure. Suppose a curious tallyman makes an additional measurement of qubits to gain information about voters. For example, to learn the vote of voter i, the easiest way is by measuring the ith qubit in computational basis. If voter i has chosen to vote, this attack will be unnoticed. But if voter i has chosen to check the anonymity with voter j, this leads their state to be transformed into (0,1) or (1,0) with equal probability of 0.5, which means anonymity check test failure. Therefore, the curious tallyman will be detected. Xiaoqiang proposed in [11] a blind signature scheme that is based on quantum computing. The scheme combines proxy and blind signatures. The scheme consists of four parties. They are Bob, who is the message signer; Charlie, who is the message owner; Alice, who prepares the proxy warrant message; and Trent, who is responsible for delivering the two particles to Bob and Charlie and verifying the signature.

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The authors used BB84 quantum protocol for key distribution. They applied quantum entanglement for the signature generation and verification process. Using a one-time pad encryption algorithm provides unconditional security and prevents eavesdropping. In [12], the authors propose a blind quantum scheme based on a two-particle entangled system. It combines proxy and blind signatures and consists of three parties. Alice is the message owner, Bob is the message signer, and Charlie is the message arbitrator and Bob’s proxy. This scheme can be used in privacy-related protocols. The authors used entanglement to the blind signature generation process and the verification process. The key distribution method is not explained in this paper.
We proposed a new scheme that enhanced an existing one that solves the check back e-voting anonymity to solve the problem of denying the value of the ballot. By implementing the concept of the entanglement between two random voters, the signer candetermine the correct value of the ballot. However, this scheme has a simple weakness which shows up when the signer (Bob) tries to contact the second voter (Nancy) asking her for the qubit, but she does not respond. We are planning to extend this scheme to address this problem by keeping the original qubits in a separate database somewhere in the system. Code Shoppy

Detection of Anomalous Behavior In An Examination Hall Towards Automated Proctoring

Exam Hall Ticket Management System

Detection of Anomalous Behavior In An Examination Hall Towards Automated Proctoring

The paper proposes a workflow for the automaticdetection of anomalous behavior in an examination hall, towards the automated proctoring of tests in classes. Certain assumptions about normal behavior in the context of proctoring exams are made. Anomalies are behavior patterns that are relatively (and significantly) different. While not every anomalous behavior may be cause for suspicion, the system is designed to detection typical patterns for actions of concern such as discussions during an exam or the turning around or the passing of notes, etc.

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This detection is based on features computed using the histogram of gradient orientations followed by a nearest -neighbor search through annotated patterns of pre- recorded clips to train the system for behavior that may cause concern. While there may be false positives, the system is intended as a decision support system to facilitate automatic proctoring of tests and deters malpractice.

In recent years, with the rapid development of computer technology, video surveillance technology has made considerable progress in many areas. But the traditional method of video surveillance is fulfilled by personnel monitoring, which is a heavy workload.Video Analytics is a technology that is used to analyze video for specific data, behavior, objects or attitude. This paper studies the automatic surveillance methodology in Examination hall, which can detect abnormal behavior in real time. It assumes that for any specific context, there is a notion of what constitutes normal behavior and conversely abnormal behavior. Anomalies stand out to be as different relative to the context of their surrounding in space of time. Hence it is a good way to solve this problem using this technology [1].
Video surveillance can be an effective tool for today’s businesses both large and small such as in video surveillance inexamination halls, security surveillance and deterring dishonest and deceitful behaviors [2].For such systems it is needed to design a core which uses a method to detect human actions, classify them based upon several actions in sequence.However, building an abnormal behavior recognition system is a challenging problem because of the variations in the quality of the video, environment size and certain postures of the humans. Environment of the examination room is crowded and dynamic, which imposes challenge for current approaches to video action detection because it is difficult to segment the student from the background due to disrupting motion from other objects and the scene. All of these make it difficult to satisfy the application in the real-world scene [5].Some of the main applications of Human Behavior detection are crime detection in sparsely populated areas like ATMs, automated sports commentary, intrusion detection, and detection of jaywalking on roads, characterization of human gait, person counting in a crowd, gender classification and fall detection for elderly people. In today’s world with increase in technology, systems can be developed and used to detect human activity. This paper aims at detecting human activities which are then classified into different categories. These categories are then studied, identified and interpreted as normal or abnormal behavior. The aim focuses on developing this system to urge an increase in the security system and services provided for the security of examination authority.The paper proposes a system for abnormal activity detection in examination hall videos using K Nearest Neighbour (KNN) which encode scene rules and are used to smooth sequences of actions. High-level behavior recognition is achieved by computing the probability that a set of predefined KNN’s explains the present action sequence.
This system is deployed in an examination hall where the student’s activities and behavior are monitored on a surveillance camera. System accepts video as the input and an automatic alerting system alerts the relevant authorities when required. Based on the predefined anomalies, the activities are categorized as normal or abnormal behavior.
This paper deals with designing an approach wherein it tries to detect any abnormal behaviors present in the videos. The system first works by detecting all students present in the video. After detecting all the students, it tracks the detected students throughout the course of the video. The features of the tracked students are calculated using HoG feature descriptor and then sent to the K-Nearest Neighbor classifier. The classifier is pre-trained to detect normal or abnormal actions. System is made to be adaptable to lots of different conditions as in, a user can choose the behaviors that they want the system to detect and train the system specifically for that. This means that it can be used across a variety of situations and conditions from normal surveillance to security related monitoring. This system is also capable of detecting abnormalities if there are more than five people in an exam hall. Learn More
In the area of machine learning confusion allows visualization of the performance of an algorithm. TABLE II indicatesConfusion matrix of the system. Each column of the matrix represents the instances in an expected class while each row represents the instance of the actual class. Confusion matrix reports the number of false positives, false negatives, true positives and true negatives. This allows more exhaustive analysis than mere percentage of correct guesses. All the diagonal values indicate the accuracy of each action detected.