【学术会议前沿信息|科研必备】EI检索-JPCS出版 | 2026电子信息、通信工程、能源自动化、AI数字媒体国际会议征稿

【学术会议前沿信息|科研必备】EI检索-JPCS出版 | 2026电子信息、通信工程、能源自动化、AI数字媒体国际会议征稿

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祝大家逢考必过！逢投必中！上岸上岸上岸！upupup

大多数高校硕博生毕业要求需要参加学术会议，发表EI或者SCI检索的学术论文会议论文。详细信息可扫描博文下方二维码 “学术会议小灵通”或参考学术信息专栏：https://ais.cn/u/mmmiUz

前言

• 拥抱科技前沿，绽放学术光芒！ 在阳光海岛与创新名城的热情拥抱中，与全球学者共创智能未来！🚀

🌐 第五届电子信息与通信工程国际学术会议（EICE 2026）

2026 5th International Conference on Electronic Information and Communication Engineering

• 📅 时间：2026年1月30日-2月1日
• 📍 地点：中国-海南三亚
• ✨ 亮点：在热带天堂三亚探讨电子信息前沿，由湖南大学、暨南大学联合主办，搭建产学研合作桥梁！
• 🔍 检索：EI Compendex, Scopus
• 👥 适合投稿人群：电子信息、通信工程领域研究者，欢迎硕博生分享创新技术与工程应用！
• 代码示例：基于FFT的深度联合源信道编码算法

import torch
import torch.nn as nn
import torch.fft

class FFTDeepJSCC(nn.Module):
    """基于FFT的深度联合源信道编码 - 降低计算复杂度"""
    
    def __init__(self, input_dim=784, encoded_dim=256, fft_bins=128):
        super().__init__()
        self.input_dim = input_dim
        self.encoded_dim = encoded_dim
        
        # FFT-based 编码器替代传统CNN
        self.fft_encoder = nn.Sequential(
            nn.Linear(input_dim, fft_bins * 2),
            nn.ReLU()
        )
        
        # 频域处理层
        self.frequency_processor = nn.Linear(fft_bins, encoded_dim // 2)
        
        # 哈达玛积变换层
        self.hadamard_proj = nn.Parameter(torch.randn(encoded_dim // 2, encoded_dim // 2))
        
    def forward(self, x, noise_std=0.01):
        batch_size = x.shape[0]
        
        # FFT变换到频域
        x_flat = x.view(batch_size, -1)
        encoded = self.fft_encoder(x_flat)
        
        # 分割实部和虚部
        real_part = encoded[:, :self.encoded_dim//2]
        imag_part = encoded[:, self.encoded_dim//2:]
        
        # 频域处理
        freq_real = self.frequency_processor(real_part)
        freq_imag = self.frequency_processor(imag_part)
        
        # 哈达玛积变换
        hadamard_real = torch.matmul(freq_real, self.hadamard_proj)
        hadamard_imag = torch.matmul(freq_imag, self.hadamard_proj)
        
        # 模拟信道噪声
        noisy_real = hadamard_real + torch.randn_like(hadamard_real) * noise_std
        noisy_imag = hadamard_imag + torch.randn_like(hadamard_imag) * noise_std
        
        # 合并输出
        output = torch.cat([noisy_real, noisy_imag], dim=1)
        return output

    def compute_compression_ratio(self, original_size):
        """计算压缩比"""
        encoded_size = self.encoded_dim * 4  # 假设32位浮点数
        return original_size * 32 / (encoded_size * 32)

# 使用示例
deep_jscc = FFTDeepJSCC(input_dim=784, encoded_dim=256)
sample_input = torch.randn(1, 1, 28, 28)  # MNIST-like图像
encoded_output = deep_jscc(sample_input.view(1, -1))

print(f"输入维度: {sample_input.numel()}")
print(f"编码输出维度: {encoded_output.shape[1]}")
print(f"压缩比: {deep_jscc.compute_compression_ratio(sample_input.numel()):.2f}:1")

⚡ 第五届能源利用与自动化国际学术会议（ICEUA 2026）

2026 5th International Conference on Energy Utilization and Automation

• 📅 时间：2026年1月30日-2月1日
• 📍 地点：中国-南京
• ✨ 亮点：在创新名城南京聚焦能源自动化，围绕"智能驱动·绿色赋能"主题，推动碳中和目标实现！
• 🔍 检索：EI Compendex, Scopus
• 👥 适合投稿人群：能源利用、自动化领域学者，诚邀硕博生展示绿色技术创新成果！
• 代码示例：多智能体强化学习用于微电网能量管理

import numpy as np
import torch
import torch.nn as nn

class MicrogridMADDPG:
    """多智能体深度确定性策略梯度用于微电网能量管理"""
    
    def __init__(self, n_agents=3, state_dim=9, action_dim=3, hidden_dim=64):
        self.n_agents = n_agents
        self.state_dim = state_dim
        self.action_dim = action_dim
        
        # 演员网络（策略网络）
        self.actor_networks = nn.ModuleList([
            nn.Sequential(
                nn.Linear(state_dim, hidden_dim),
                nn.ReLU(),
                nn.Linear(hidden_dim, hidden_dim // 2),
                nn.ReLU(),
                nn.Linear(hidden_dim // 2, action_dim),
                nn.Tanh()  # 输出在[-1,1]范围内
            ) for _ in range(n_agents)
        ])
        
        # 评论家网络（值函数网络）
        self.critic_network = nn.Sequential(
            nn.Linear(state_dim + n_agents * action_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Linear(hidden_dim // 2, 1)
        )
        
    def select_actions(self, states, exploration_noise=0.1):
        """为所有智能体选择动作"""
        actions = []
        for i, (state, actor) in enumerate(zip(states, self.actor_networks)):
            with torch.no_grad():
                action = actor(state)
                # 添加探索噪声
                noise = torch.randn_like(action) * exploration_noise
                action = torch.clamp(action + noise, -1.0, 1.0)
                actions.append(action)
        return torch.stack(actions)
    
    def compute_reward(self, states, actions, next_states):
        """计算多目标奖励函数"""
        reward_components = {}
        
        # 1. 能源成本奖励（最小化购电成本）
        grid_power = states[:, 0]  # 电网功率
        electricity_price = states[:, 1]  # 电价
        energy_cost = torch.abs(grid_power) * electricity_price
        reward_components['cost'] = -energy_cost.mean()
        
        # 2. 可再生能源利用奖励（最大化自消纳）
        solar_generation = states[:, 2]  # 太阳能发电
        load_demand = states[:, 3]  # 负载需求
        renewable_utilization = torch.min(solar_generation, load_demand) / (load_demand + 1e-8)
        reward_components['renewable'] = renewable_utilization.mean()
        
        # 3. 电网稳定性奖励（最小化功率波动）
        power_fluctuation = torch.abs(next_states[:, 0] - states[:, 0])
        reward_components['stability'] = -power_fluctuation.mean()
        
        # 加权综合奖励
        total_reward = (0.5 * reward_components['cost'] + 
                       0.3 * reward_components['renewable'] + 
                       0.2 * reward_components['stability'])
        
        return total_reward, reward_components

class MicrogridEnvironment:
    """微电网仿真环境"""
    
    def __init__(self):
        self.solar_capacity = 100.0  # kW
        self.battery_capacity = 200.0  # kWh
        self.max_grid_power = 50.0  # kW
        
    def reset(self):
        """重置环境状态"""
        state = {
            'grid_power': np.random.uniform(-self.max_grid_power, self.max_grid_power),
            'electricity_price': np.random.uniform(0.1, 0.3),  # $/kWh
            'solar_generation': np.random.uniform(0, self.solar_capacity),
            'load_demand': np.random.uniform(10, 80),
            'battery_soc': np.random.uniform(0.2, 0.8),
            'time_of_day': np.random.uniform(0, 24)
        }
        return torch.FloatTensor(list(state.values()))
    
    def step(self, states, actions):
        """环境状态转移"""
        next_states = []
        for state, action in zip(states, actions):
            # 简化状态更新逻辑
            new_state = state.clone()
            
            # 应用控制动作（电池充放电、负荷调整等）
            battery_action = action[0]
            load_shift_action = action[1]
            grid_action = action[2]
            
            # 更新状态（简化物理模型）
            new_state[0] += grid_action * 10  # 电网功率调整
            new_state[4] += battery_action * 0.1  # 电池SOC更新
            new_state[3] += load_shift_action * 5  # 负荷需求调整
            
            # 确保状态在合理范围内
            new_state[0] = torch.clamp(new_state[0], -self.max_grid_power, self.max_grid_power)
            new_state[4] = torch.clamp(new_state[4], 0.0, 1.0)
            
            next_states.append(new_state)
        
        return torch.stack(next_states)

# 使用示例
maddpg = MicrogridMADDPG(n_agents=2)
env = MicrogridEnvironment()

# 模拟多智能体交互
states = torch.stack([env.reset() for _ in range(2)])
actions = maddpg.select_actions(states)
next_states = env.step(states, actions)

reward, components = maddpg.compute_reward(states, actions, next_states)

print(f"总奖励: {reward:.4f}")
print(f"奖励分量: { {k: v.item() for k, v in components.items()} }")
print(f"智能体动作: {actions.detach().numpy()}")

🤖 第二届人工智能、数字媒体技术与社会计算国际学术会议（ICAIDS 2026）

The 2nd International Conference on Artificial Intelligence, Digital Media Technology and Social Computing

• 📅 时间：2026年1月30日-2月1日
• 📍 地点：中国-三亚 / 美国-芝加哥
• ✨ 亮点：中美双会场探讨AI与数字媒体融合，克莱姆森大学主办，促进跨学科国际合作！
• 🔍 检索：EI Compendex, Scopus
• 👥 适合投稿人群：人工智能、数字媒体领域研究者，期待硕博生分享社会计算创新研究！
• 代码示例：跨模态社会计算分析框架

import torch
import torch.nn as nn
import torch.nn.functional as F

class CrossModalSocialComputing(nn.Module):
    """跨模态社会计算分析框架 - 融合文本、图像和社会网络数据"""
    
    def __init__(self, text_dim=300, image_dim=512, social_dim=128, hidden_dim=256):
        super().__init__()
        
        # 文本编码器（简化版BERT）
        self.text_encoder = nn.Sequential(
            nn.Linear(text_dim, hidden_dim),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(hidden_dim, hidden_dim // 2)
        )
        
        # 图像编码器（简化版ResNet）
        self.image_encoder = nn.Sequential(
            nn.Linear(image_dim, hidden_dim),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(hidden_dim, hidden_dim // 2)
        )
        
        # 社会网络编码器
        self.social_encoder = nn.Sequential(
            nn.Linear(social_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Linear(hidden_dim // 2, hidden_dim // 4)
        )
        
        # 跨模态注意力融合
        self.cross_modal_attention = nn.MultiheadAttention(
            embed_dim=hidden_dim // 2, num_heads=4, batch_first=True
        )
        
        # 社会计算预测头
        self.social_predictor = nn.Sequential(
            nn.Linear(hidden_dim // 2 + hidden_dim // 4, hidden_dim // 2),
            nn.ReLU(),
            nn.Linear(hidden_dim // 2, 3)  # 3个社会计算任务
        )
        
    def forward(self, text_data, image_data, social_data):
        # 编码各模态数据
        text_features = self.text_encoder(text_data)
        image_features = self.image_encoder(image_data)
        social_features = self.social_encoder(social_data)
        
        # 跨模态注意力融合
        multimodal_sequence = torch.stack([text_features, image_features], dim=1)
        attended_multimodal, _ = self.cross_modal_attention(
            multimodal_sequence, multimodal_sequence, multimodal_sequence
        )
        
        # 聚合多模态特征
        aggregated_multimodal = torch.mean(attended_multimodal, dim=1)
        
        # 融合社会网络特征
        combined_features = torch.cat([aggregated_multimodal, social_features], dim=1)
        
        # 社会计算预测
        predictions = self.social_predictor(combined_features)
        
        return {
            'virality_score': predictions[:, 0],  # 内容传播度
            'sentiment_trend': predictions[:, 1],  # 情感趋势
            'community_impact': predictions[:, 2]  # 社区影响力
        }

class SocialDataProcessor:
    """社会计算数据处理器"""
    
    def __init__(self, max_users=1000, max_posts=5000):
        self.max_users = max_users
        self.max_posts = max_posts
        
    def simulate_social_data(self, batch_size=32):
        """模拟多模态社会媒体数据"""
        # 文本数据（词向量）
        text_data = torch.randn(batch_size, 300)
        
        # 图像数据（特征向量）
        image_data = torch.randn(batch_size, 512)
        
        # 社会网络数据（用户关系、交互行为）
        social_data = torch.randn(batch_size, 128)
        
        return text_data, image_data, social_data
    
    def compute_social_metrics(self, predictions, ground_truth=None):
        """计算社会计算指标"""
        metrics = {}
        
        # 预测准确率（如果有真实标签）
        if ground_truth is not None:
            for key in predictions.keys():
                correlation = torch.corrcoef(torch.stack([
                    predictions[key], ground_truth[key]
                ]))[0, 1]
                metrics[f'{key}_correlation'] = correlation.item()
        
        # 预测分布统计
        for key, values in predictions.items():
            metrics[f'{key}_mean'] = torch.mean(values).item()
            metrics[f'{key}_std'] = torch.std(values).item()
        
        return metrics

# 使用示例
social_model = CrossModalSocialComputing()
data_processor = SocialDataProcessor()

# 生成模拟数据
text_data, image_data, social_data = data_processor.simulate_social_data(batch_size=16)

# 社会计算分析
predictions = social_model(text_data, image_data, social_data)
metrics = data_processor.compute_social_metrics(predictions)

print("社会计算预测结果:")
for key, values in predictions.items():
    print(f"{key}: 均值={values.mean():.4f}, 标准差={values.std():.4f}")

print("\n社会计算指标:")
for key, value in metrics.items():
    print(f"{key}: {value:.4f}")

💻 2026年人工智能与数字服务国际会议（ICADS 2026）

2026 International Conference on Artificial Intelligence and Digital Services

• 📅 时间：2026年2月6-8日
• 📍 地点：中国-昆明
• ✨ 亮点：在春城昆明聚焦AI与数字服务创新，探讨智能算法与人机交互前沿技术！
• 🔍 检索：EI Compendex, Scopus, Google Scholar
• 👥 适合投稿人群：人工智能、数字服务领域学者，欢迎硕博生展示服务计算创新应用！
• 代码示例：基于深度Q学习的数字服务自适应组合算法

import numpy as np
import torch
import torch.nn as nn
import collections

class DigitalServiceDQN(nn.Module):
    """深度Q网络用于数字服务自适应组合"""
    
    def __init__(self, state_dim, action_dim, hidden_dim=128):
        super().__init__()
        self.state_dim = state_dim
        self.action_dim = action_dim
        
        # Q网络架构
        self.q_network = nn.Sequential(
            nn.Linear(state_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, action_dim)
        )
        
        # 目标网络（稳定训练）
        self.target_network = nn.Sequential(
            nn.Linear(state_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, action_dim)
        )
        
        # 同步目标网络
        self.target_network.load_state_dict(self.q_network.state_dict())
        
    def forward(self, state, use_target=False):
        if use_target:
            return self.target_network(state)
        return self.q_network(state)

class ServiceCompositionEnvironment:
    """数字服务组合环境"""
    
    def __init__(self, n_services=10, n_qos=4):
        self.n_services = n_services
        self.n_qos = n_qos
        
        # 服务QoS属性（响应时间、可用性、成本、可靠性）
        self.service_qos = np.random.uniform(0.5, 1.0, (n_services, n_qos))
        
        # 用户需求
        self.user_requirements = np.random.uniform(0.7, 1.0, n_qos)
        
    def reset(self):
        """重置环境状态"""
        self.current_services = np.zeros(self.n_services, dtype=bool)
        self.step_count = 0
        return self._get_state()
    
    def _get_state(self):
        """获取当前状态"""
        state = np.zeros(self.state_dim)
        
        # 已选服务状态
        state[:self.n_services] = self.current_services.astype(float)
        
        # 当前QoS聚合值
        if np.sum(self.current_services) > 0:
            selected_qos = self.service_qos[self.current_services]
            aggregated_qos = np.mean(selected_qos, axis=0)
            state[self.n_services:self.n_services+self.n_qos] = aggregated_qos
        else:
            state[self.n_services:self.n_services+self.n_qos] = 0
        
        # 用户需求差异
        state[self.n_services+self.n_qos:] = self.user_requirements
        
        return torch.FloatTensor(state)
    
    @property
    def state_dim(self):
        return self.n_services + self.n_qos * 2
    
    @property
    def action_dim(self):
        return self.n_services + 1  # 选择服务或完成组合
    
    def step(self, action):
        """执行动作并返回新状态和奖励"""
        reward = 0
        done = False
        
        if action < self.n_services:  # 选择服务
            if not self.current_services[action]:
                self.current_services[action] = True
                reward = self._calculate_reward()
            else:
                reward = -1  # 惩罚重复选择
        else:  # 完成组合
            done = True
            reward = self._calculate_final_reward()
        
        self.step_count += 1
        if self.step_count >= self.n_services:
            done = True
        
        return self._get_state(), reward, done
    
    def _calculate_reward(self):
        """计算即时奖励"""
        if np.sum(self.current_services) == 0:
            return 0
        
        selected_qos = self.service_qos[self.current_services]
        aggregated_qos = np.mean(selected_qos, axis=0)
        
        # QoS满足度
        qos_satisfaction = 1.0 - np.mean(np.maximum(0, self.user_requirements - aggregated_qos))
        
        # 服务数量惩罚（鼓励精简组合）
        service_penalty = -0.1 * np.sum(self.current_services)
        
        return qos_satisfaction + service_penalty
    
    def _calculate_final_reward(self):
        """计算最终奖励"""
        if np.sum(self.current_services) == 0:
            return -10
        
        selected_qos = self.service_qos[self.current_services]
        aggregated_qos = np.mean(selected_qos, axis=0)
        
        # 最终QoS满足度
        qos_satisfaction = 1.0 - np.mean(np.maximum(0, self.user_requirements - aggregated_qos))
        
        # 组合效率奖励
        efficiency = 1.0 / (1 + np.sum(self.current_services))
        
        return 10 * qos_satisfaction + 5 * efficiency

class ServiceCompositionAgent:
    """数字服务组合智能体"""
    
    def __init__(self, state_dim, action_dim, learning_rate=0.001, gamma=0.99):
        self.dqn = DigitalServiceDQN(state_dim, action_dim)
        self.optimizer = torch.optim.Adam(self.dqn.parameters(), lr=learning_rate)
        self.gamma = gamma
        self.memory = collections.deque(maxlen=10000)
        
    def remember(self, state, action, reward, next_state, done):
        """存储经验回放"""
        self.memory.append((state, action, reward, next_state, done))
    
    def act(self, state, epsilon=0.1):
        """选择动作（ε-贪婪策略）"""
        if np.random.random() < epsilon:
            return np.random.randint(0, self.dqn.action_dim)
        else:
            with torch.no_grad():
                q_values = self.dqn(state.unsqueeze(0))
                return q_values.argmax().item()
    
    def replay(self, batch_size=32):
        """经验回放训练"""
        if len(self.memory) < batch_size:
            return
        
        batch = np.random.choice(len(self.memory), batch_size, replace=False)
        batch = [self.memory[i] for i in batch]
        
        states = torch.stack([exp[0] for exp in batch])
        actions = torch.LongTensor([exp[1] for exp in batch])
        rewards = torch.FloatTensor([exp[2] for exp in batch])
        next_states = torch.stack([exp[3] for exp in batch])
        dones = torch.BoolTensor([exp[4] for exp in batch])
        
        # 计算当前Q值
        current_q = self.dqn(states).gather(1, actions.unsqueeze(1))
        
        # 计算目标Q值
        with torch.no_grad():
            next_q = self.dqn(next_states, use_target=True).max(1)[0]
            target_q = rewards + self.gamma * next_q * (~dones)
        
        # 计算损失并更新
        loss = nn.MSELoss()(current_q.squeeze(), target_q)
        
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

# 使用示例
env = ServiceCompositionEnvironment(n_services=8, n_qos=4)
agent = ServiceCompositionAgent(env.state_dim, env.action_dim)

# 模拟训练过程
state = env.reset()
total_reward = 0

for step in range(20):
    action = agent.act(state, epsilon=0.1)
    next_state, reward, done = env.step(action)
    
    agent.remember(state, action, reward, next_state, done)
    agent.replay()
    
    total_reward += reward
    state = next_state
    
    if done:
        break

print(f"服务组合完成，总奖励: {total_reward:.2f}")
print(f"最终选择服务数量: {np.sum(env.current_services)}")
print(f"选择的服务索引: {np.where(env.current_services)[0]}")

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