diff --git a/README.md b/README.md
index c13c485..69a767d 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@
 # Analysis Platform
 
 ```
-2023-11-10: Released version 4.5.0! see RELEASE.md for details.
+2024-01-15: Released version 4.5.1! see RELEASE.md for details.
 ```
 
 Analysis Platform is an open source web application to import, connect and visualize factory IoT data. It helps to collect, link and integrate data from multiple data sources.
diff --git a/RELEASE.md b/RELEASE.md
index df92776..06a3d30 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -1,5 +1,32 @@
 # Releases
 
+## v4.5.1
+
+This version is a minor update including some bug fixes
+
+Improvements
+
+* Common
+  * Support new format of datetime: 'dd/mm/YYYY'
+  * Detect serial column and auto-check in Process Config page
+
+* V2 data
+  * Enable importing 'WorkType, Quality, LotNo, TrayNo'
+  * Enable importing alphabetical column names
+  * Extract vertical data without measurement label in value: '計測値:|measurement.'
+  * Support to detect process name if there is no value
+  * Enable selecting/importing duplicated column names
+
+Bug fixes
+
+* Common
+  * Fixed to appropriately modify English Name of Process Config page
+  * Fixed an issue when importing same column name from CSV data
+  * Fixed an issue of AgP page about summarized data using tz_convert for datetime
+* V2 data
+  * Fixed to import abnormal columns
+  * Fixed issue of 'undefined' value when preview data from Process Config page
+
 ## v4.5.0
 
 Core changes
diff --git a/VERSION b/VERSION
index 018b130..8da8af6 100644
--- a/VERSION
+++ b/VERSION
@@ -1,4 +1,4 @@
-v4.5.0.206.eccc3d8c
+v4.5.1.faa01916
 1
 OSS
 
diff --git a/ap/api/aggregate_plot/services.py b/ap/api/aggregate_plot/services.py
index a3979a2..1886466 100644
--- a/ap/api/aggregate_plot/services.py
+++ b/ap/api/aggregate_plot/services.py
@@ -98,7 +98,7 @@ def gen_agp_data(dic_param: DicParam):
 
     graph_param: DicParam
     if graph_param.common.divide_format is not None:
-        df = convert_utc_to_local_time_and_offset(df, graph_param)
+        # df = convert_utc_to_local_time_and_offset(df, graph_param)
         df = gen_divide_format_column(
             df, graph_param.common.divide_calendar_dates, graph_param.common.divide_calendar_labels
         )
@@ -181,7 +181,7 @@ def gen_divide_format_column(
     if df.empty:
         return df
     df.sort_values(Cycle.time.key, inplace=True)
-    dt = df[Cycle.time.key]
+    dt = pd.to_datetime(df[Cycle.time.key])
     divide_calendar_dates = pd.to_datetime(divide_calendar_dates, utc=True)
     for i, label in enumerate(divide_calendar_labels):
         start_time = divide_calendar_dates[i]
diff --git a/ap/api/heatmap/services.py b/ap/api/heatmap/services.py
index 2bc8333..c257adb 100644
--- a/ap/api/heatmap/services.py
+++ b/ap/api/heatmap/services.py
@@ -45,6 +45,7 @@
     NA_STR,
     NOT_EXACT_MATCH_FILTER_IDS,
     TIME_COL,
+    TIME_COL_LOCAL,
     UNIQUE_CATEGORIES,
     UNIQUE_SERIAL,
     UNMATCHED_FILTER_IDS,
@@ -138,7 +139,7 @@ def get_utc_offset(time_zone):
     return time_offset
 
 
-def limit_num_cells(df_cells: pd.DataFrame, end_tm, client_tz, limit=10000):
+def limit_num_cells(df_cells: pd.DataFrame, end_tm, limit=10000):
     """Limit number of cells to 10k including empty cells"""
     # is_res_limited = df_cells.index.size > limit
 
@@ -148,7 +149,7 @@ def limit_num_cells(df_cells: pd.DataFrame, end_tm, client_tz, limit=10000):
     # update new end_time to 10000 cells
     last_cell_time = list(df_cells.tail(1)[TIME_COL])[0]
     # end_tm is utc -> convert to local-time
-    end_tm_tz = pd.to_datetime(pd.Series([end_tm]), utc=True).dt.tz_convert(client_tz)
+    end_tm_tz = pd.to_datetime(pd.Series([end_tm]), utc=True)
     end_tm_tz = list(end_tm_tz)[0]
     new_end_time = np.minimum(end_tm_tz, last_cell_time)
     new_end_tm = new_end_time.strftime(DATE_FORMAT_QUERY)
@@ -376,21 +377,24 @@ def gen_plotly_data(
 
 @log_execution_time()
 @abort_process_handler()
-def gen_agg_col(df: pd.DataFrame, hm_mode, hm_step):
+def gen_agg_col(df: pd.DataFrame, hm_mode, hm_step, client_tz):
     """Aggregate data by time"""
     pd_step = convert_to_pandas_step(hm_step, hm_mode)
+    df[TIME_COL_LOCAL] = pd.to_datetime(df[TIME_COL], utc=True).dt.tz_convert(tz=client_tz)
     print(df.index.size)
     if hm_mode == 7:
         # .astype(str).str[:13] or 16 sometimes doesn't work as expected
-        df[AGG_COL] = df[TIME_COL].dt.floor(pd_step).dt.strftime('%Y-%m-%d %H')
+        df[AGG_COL] = df[TIME_COL_LOCAL].dt.floor(pd_step).dt.strftime('%Y-%m-%d %H')
     else:
-        df[AGG_COL] = df[TIME_COL].dt.floor(pd_step).dt.strftime('%Y-%m-%d %H:%M')
+        df[AGG_COL] = df[TIME_COL_LOCAL].dt.floor(pd_step).dt.strftime('%Y-%m-%d %H:%M')
     return df
 
 
 def gen_weekly_ticks(df: pd.DataFrame):
     # tick weekly, first day of week, sunday
-    df['x_label'] = df[TIME_COL] - (df[TIME_COL].dt.weekday % 7) * np.timedelta64(1, 'D')
+    df['x_label'] = df[TIME_COL_LOCAL] - (df[TIME_COL_LOCAL].dt.weekday % 7) * np.timedelta64(
+        1, 'D'
+    )
     df['x_label'] = (
         get_year_week_in_df_column(df['x_label'])
         + '<br>'
@@ -404,11 +408,11 @@ def gen_weekly_ticks(df: pd.DataFrame):
 def gen_daily_ticks(df: pd.DataFrame):
     # tick weekly, first day of week, sunday
     df['x_label'] = (
-        get_year_week_in_df_column(df[TIME_COL])
+        get_year_week_in_df_column(df[TIME_COL_LOCAL])
         + '<br>'
-        + df[TIME_COL].dt.month.astype(str).str.pad(2, fillchar='0')
+        + df[TIME_COL_LOCAL].dt.month.astype(str).str.pad(2, fillchar='0')
         + '-'
-        + df[TIME_COL].dt.day.astype(str).str.pad(2, fillchar='0')
+        + df[TIME_COL_LOCAL].dt.day.astype(str).str.pad(2, fillchar='0')
     )
     return df['x_label']
 
@@ -422,11 +426,6 @@ def get_year_week_in_df_column(column: pd.DataFrame.columns):
     )
 
 
-def convert_cell_tz(df: pd.DataFrame, offset):
-    df[TIME_COL] = df[TIME_COL] + offset
-    return df
-
-
 @log_execution_time()
 @abort_process_handler()
 def gen_x_y(df: pd.DataFrame, hm_mode, hm_step, start_tm, end_tm):
@@ -439,18 +438,18 @@ def gen_x_y(df: pd.DataFrame, hm_mode, hm_step, start_tm, end_tm):
     if hm_mode == 7:
         # gen y
         row_per_day = int(24 / hm_step)
-        df['dayofweek'] = df[TIME_COL].dt.day_name().astype(str).str[:3]
-        df['newdayofweek'] = (16 - df[TIME_COL].dt.dayofweek) % 10  # mon, tue... sat
+        df['dayofweek'] = df[TIME_COL_LOCAL].dt.day_name().astype(str).str[:3]
+        df['newdayofweek'] = (16 - df[TIME_COL_LOCAL].dt.dayofweek) % 10  # mon, tue... sat
         df['y'] = (
             int(24 / hm_step)
-            - (df[TIME_COL].dt.hour / hm_step).astype(int)
+            - (df[TIME_COL_LOCAL].dt.hour / hm_step).astype(int)
             + df['newdayofweek'] * row_per_day
         )
 
         # gen x
-        df['year'] = df[TIME_COL].dt.year
+        df['year'] = df[TIME_COL_LOCAL].dt.year
         min_year = df['year'].min()
-        df['x'] = df[TIME_COL].dt.strftime('%U').astype(int) + (df['year'] % min_year) * 53
+        df['x'] = df[TIME_COL_LOCAL].dt.strftime('%U').astype(int) + (df['year'] % min_year) * 53
 
         # x_label
         if num_days <= 140:
@@ -458,62 +457,64 @@ def gen_x_y(df: pd.DataFrame, hm_mode, hm_step, start_tm, end_tm):
         elif num_days <= 365 * 2:
             # tick monthly
             df['x_label'] = (
-                get_year_week_in_df_column(df[TIME_COL])
+                get_year_week_in_df_column(df[TIME_COL_LOCAL])
                 + '<br>'
-                + df[TIME_COL].dt.month.astype(str).str.pad(2, fillchar='0')
+                + df[TIME_COL_LOCAL].dt.month.astype(str).str.pad(2, fillchar='0')
                 + '-01'
             )
         else:
             # tick yearly
-            df['x_label'] = get_year_week_in_df_column(df[TIME_COL]) + '<br>01-01'
+            df['x_label'] = get_year_week_in_df_column(df[TIME_COL_LOCAL]) + '<br>01-01'
     else:
         # gen y
         num_rows = int(1440 / hm_step)
         row_per_hour = 60 / hm_step
-        df['dayofweek'] = df[TIME_COL].dt.day_name().astype(str).str[:3]
+        df['dayofweek'] = df[TIME_COL_LOCAL].dt.day_name().astype(str).str[:3]
         if hm_step > 60:
             df['y'] = num_rows - (
-                ((df[TIME_COL].dt.minute + df[TIME_COL].dt.hour * 60) / hm_step).astype(float)
+                ((df[TIME_COL_LOCAL].dt.minute + df[TIME_COL_LOCAL].dt.hour * 60) / hm_step).astype(
+                    float
+                )
             )
         else:
             df['y'] = num_rows - (
-                (df[TIME_COL].dt.minute / hm_step).astype(int)
-                + (df[TIME_COL].dt.hour * row_per_hour).astype(int)
+                (df[TIME_COL_LOCAL].dt.minute / hm_step).astype(int)
+                + (df[TIME_COL_LOCAL].dt.hour * row_per_hour).astype(int)
             )
 
         # gen x
-        df['year'] = df[TIME_COL].dt.year
+        df['year'] = df[TIME_COL_LOCAL].dt.year
         min_year = df['year'].min()
-        df['x'] = df[TIME_COL].dt.dayofyear + 366 * (df['year'] % min_year)
+        df['x'] = df[TIME_COL_LOCAL].dt.dayofyear + 366 * (df['year'] % min_year)
 
         # x_label
         if num_days <= 21:
             # tick daily
             df['x_label'] = (
-                get_year_week_in_df_column(df[TIME_COL])
+                get_year_week_in_df_column(df[TIME_COL_LOCAL])
                 + '<br>'
-                + df[TIME_COL].dt.date.astype(str).str[5:]
+                + df[TIME_COL_LOCAL].dt.date.astype(str).str[5:]
             )
         elif num_days <= 140:
             df['x_label'] = gen_daily_ticks(df)
         elif num_days <= 365 * 2:
             # tick monthly
             df['x_label'] = (
-                get_year_week_in_df_column(df[TIME_COL])
+                get_year_week_in_df_column(df[TIME_COL_LOCAL])
                 + '<br>'
-                + df[TIME_COL].dt.month.astype(str).str.pad(2, fillchar='0')
+                + df[TIME_COL_LOCAL].dt.month.astype(str).str.pad(2, fillchar='0')
                 + '-01'
             )
         else:
             # tick yearly
-            df['x_label'] = get_year_week_in_df_column(df[TIME_COL]) + '<br>01-01'
+            df['x_label'] = get_year_week_in_df_column(df[TIME_COL_LOCAL]) + '<br>01-01'
 
     time_fmt = '%Y-%m-%d %a %H:%M'
-    df['from'] = 'From: ' + df[TIME_COL].dt.strftime(time_fmt) + '<br>'
+    df['from'] = 'From: ' + df[TIME_COL_LOCAL].dt.strftime(time_fmt) + '<br>'
     unit = 'min' if hm_mode == 1 else 'h'
-    df['to_temp'] = df[TIME_COL] + pd.to_timedelta(hm_step, unit=unit)
+    df['to_temp'] = df[TIME_COL_LOCAL] + pd.to_timedelta(hm_step, unit=unit)
     df.loc[df['to_temp'].astype(str).str[11:16] == '00:00', 'to'] = (
-        df['to_temp'].astype(str).str[:8] + df[TIME_COL].dt.strftime('%d %a ') + '24:00'
+        df['to_temp'].astype(str).str[:8] + df[TIME_COL_LOCAL].dt.strftime('%d %a ') + '24:00'
     )
     df.loc[df['to_temp'].astype(str).str[11:16] != '00:00', 'to'] = df['to_temp'].dt.strftime(
         time_fmt
@@ -674,16 +675,26 @@ def convert_to_pandas_step(hm_step, hm_mode):
 
 @log_execution_time()
 @abort_process_handler()
-def create_agg_column(df, pd_step='4h', agg_col=AGG_COL, hm_mode=7, client_tz=tz.tzutc()):
+def create_agg_column(df, agg_col=AGG_COL, hm_mode=7, hm_step=4, df_cells=None):
     """Create aggregate column data"""
-    if hm_mode == 7:
-        length = 13
-    else:
-        length = 16
-    temp = pd.to_datetime(df[TIME_COL], format='%Y-%m-%dT%H:%M', utc=True).dt.tz_convert(
-        tz=client_tz
+    dt = pd.to_datetime(df[TIME_COL], format='%Y-%m-%dT%H:%M', utc=True)
+    df[agg_col] = None
+    #
+    group_list = df_cells[TIME_COL].tolist()
+    next_cell = (
+        group_list[-1] + pd.Timedelta(minutes=hm_step)
+        if hm_mode == 1
+        else group_list[-1] + pd.Timedelta(hours=hm_step)
     )
-    df[agg_col] = temp.dt.floor(pd_step).astype(str).str[:length]
+    group_list.append(next_cell)
+    group_list = pd.to_datetime(group_list, format='%Y-%m-%dT%H:%M', utc=True)
+
+    labels = df_cells[AGG_COL].tolist()
+    for i, label in enumerate(labels):
+        start_time = group_list[i]
+        end_time = group_list[i + 1]
+        start_index, end_index = dt.searchsorted([start_time, end_time])
+        df[start_index:end_index][agg_col] = label
     return df
 
 
@@ -819,14 +830,13 @@ def gen_heatmap_data_as_dict(
     df_cells = pd.DataFrame({TIME_COL: cells})
     # time_delta = calc_time_delta(hm_mode, hm_step, start_tm)
     if not df_cells.empty:
-        df_cells[TIME_COL] = pd.to_datetime(df_cells[TIME_COL], utc=True).dt.tz_convert(
-            tz=client_tz
-        )
-    df_cells = gen_agg_col(df_cells, hm_mode, hm_step)
+        df_cells[TIME_COL] = pd.to_datetime(df_cells[TIME_COL], utc=True)
+
+    df_cells = gen_agg_col(df_cells, hm_mode, hm_step, client_tz)
 
     # limit to 10000 cells
     dic_param.update({ACT_CELLS: df_cells.index.size})
-    df_cells, end_tm = limit_num_cells(df_cells, end_tm, client_tz)
+    df_cells, end_tm = limit_num_cells(df_cells, end_tm)
 
     # generate x, y, x_label, y_label
     df_cells = gen_x_y(df_cells, hm_mode, hm_step, start_tm, end_tm)
@@ -872,8 +882,7 @@ def gen_heatmap_data_as_dict(
     dic_param[ACTUAL_RECORD_NUMBER] = actual_record_number
 
     # gen aggregate end col
-    pd_step = convert_to_pandas_step(hm_step, hm_mode)
-    df: pd.DataFrame = create_agg_column(df, pd_step, AGG_COL, hm_mode, client_tz)
+    df: pd.DataFrame = create_agg_column(df, AGG_COL, hm_mode, hm_step, df_cells)
     agg_cols = gen_agg_col_names(var_agg_cols)  # move
 
     dic_df_proc = {}
diff --git a/ap/api/setting_module/services/autolink.py b/ap/api/setting_module/services/autolink.py
index 889e112..9d57d64 100644
--- a/ap/api/setting_module/services/autolink.py
+++ b/ap/api/setting_module/services/autolink.py
@@ -19,6 +19,7 @@
 from ap.common.common_utils import detect_encoding, get_csv_delimiter, get_latest_files
 from ap.common.constants import (
     DF_CHUNK_SIZE,
+    DUMMY_V2_PROCESS_NAME,
     MAXIMUM_PROCESSES_ORDER_FILES,
     REVERSED_WELL_KNOWN_COLUMNS,
     DataGroupType,
@@ -165,12 +166,12 @@ def drop_duplicates(df: DataFrame) -> DataFrame:
 
     @log_execution_time(LOG_PREFIX)
     def __read_v2(self, file: Union[Path, str], processes: List[str], ids: List[int]):
-        datasource_type, is_abnormal_v2 = get_v2_datasource_type_from_file(file)
+        datasource_type, is_abnormal_v2, is_en_cols = get_v2_datasource_type_from_file(file)
         if datasource_type not in [DBType.V2, DBType.V2_MULTI, DBType.V2_HISTORY]:
             return
 
         process_col = get_reversed_column_value_from_v2(
-            datasource_type.name, DataGroupType.PROCESS_NAME.value, is_abnormal_v2
+            datasource_type.name, DataGroupType.PROCESS_NAME.value, is_abnormal_v2, is_en_cols
         )
         serial_col = get_reversed_column_value_from_v2(
             datasource_type.name, DataGroupType.DATA_SERIAL.value, is_abnormal_v2
@@ -202,6 +203,8 @@ def __read_v2(self, file: Union[Path, str], processes: List[str], ids: List[int]
                 file, chunksize=DF_CHUNK_SIZE, nrows=AUTOLINK_TOTAL_RECORDS_PER_SOURCE, **params
             ) as reader:
                 for df_chunk in reader:
+                    if DUMMY_V2_PROCESS_NAME in mapping_processes_id:
+                        df_chunk[process_col] = df_chunk[process_col].fillna(DUMMY_V2_PROCESS_NAME)
                     df_processes = df_chunk[df_chunk[process_col].isin(mapping_processes_id)]
                     df_processes = df_processes.rename(columns=rename_params)
 
@@ -226,6 +229,8 @@ def __read_v2(self, file: Union[Path, str], processes: List[str], ids: List[int]
                 **params,
             ) as reader:
                 for df_chunk in reader:
+                    if DUMMY_V2_PROCESS_NAME in mapping_processes_id:
+                        df_chunk[process_col] = df_chunk[process_col].fillna(DUMMY_V2_PROCESS_NAME)
                     df_processes = df_chunk[df_chunk[process_col].isin(mapping_processes_id)]
                     df_processes = df_processes.rename(columns=rename_params)
 
diff --git a/ap/api/setting_module/services/csv_import.py b/ap/api/setting_module/services/csv_import.py
index 29a1267..7b5f26f 100644
--- a/ap/api/setting_module/services/csv_import.py
+++ b/ap/api/setting_module/services/csv_import.py
@@ -62,7 +62,10 @@
 from ap.common.logger import log_execution_time
 from ap.common.scheduler import JobType, scheduler_app_context
 from ap.common.services.csv_content import is_normal_csv, read_data
-from ap.common.services.csv_header_wrapr import add_suffix_if_duplicated
+from ap.common.services.csv_header_wrapr import (
+    add_suffix_if_duplicated,
+    transform_duplicated_col_suffix_to_pandas_col,
+)
 from ap.common.services.normalization import normalize_list, normalize_str
 from ap.common.timezone_utils import (
     add_days_from_utc,
@@ -245,6 +248,8 @@ def import_csv(proc_id, record_per_commit=RECORD_PER_COMMIT, is_user_request=Non
 
         # check missing columns
         if is_abnormal is False:
+            dic_csv_cols = None
+            dic_org_csv_cols = None
             csv_cols = headers
             # in case if v2, assume that there is not missing columns from v2 files
             if not is_v2_datasource:
@@ -256,9 +261,13 @@ def import_csv(proc_id, record_per_commit=RECORD_PER_COMMIT, is_user_request=Non
                     delimiter=transformed_file_delimiter,
                     do_normalize=False,
                 )
-                csv_cols = next(check_file)
-                csv_cols = normalize_list(csv_cols)
-                csv_cols = add_suffix_if_duplicated(csv_cols, True)
+                org_csv_cols = next(check_file)
+                csv_cols = normalize_list(org_csv_cols)
+                csv_cols, with_dupl_cols = add_suffix_if_duplicated(csv_cols, True)
+                dic_csv_cols = dict(zip(csv_cols, with_dupl_cols))
+                # add suffix to origin csv cols
+                org_csv_cols, _ = add_suffix_if_duplicated(org_csv_cols, True)
+                dic_org_csv_cols = dict(zip(csv_cols, org_csv_cols))
 
                 check_file.close()
             # missing_cols = set(dic_use_cols).difference(csv_cols)
@@ -266,7 +275,13 @@ def import_csv(proc_id, record_per_commit=RECORD_PER_COMMIT, is_user_request=Non
             valid_columns = list(set(dic_use_cols).intersection(csv_cols))
             # re-arrange cols
             valid_columns = [col for col in csv_cols if col in valid_columns]
+            dic_valid_csv_cols = dict(zip(valid_columns, [False] * len(valid_columns)))
             missing_cols = [] if len(valid_columns) else dic_use_cols
+
+            if not is_v2_datasource:
+                valid_with_dupl_cols = [dic_csv_cols[col] for col in valid_columns]
+                dic_valid_csv_cols = dict(zip(valid_columns, valid_with_dupl_cols))
+
             if DATETIME_DUMMY in missing_cols:
                 # remove dummy col before check
                 missing_cols.remove(DATETIME_DUMMY)
@@ -307,7 +322,10 @@ def import_csv(proc_id, record_per_commit=RECORD_PER_COMMIT, is_user_request=Non
                 continue
 
             # default_csv_param['usecols'] = [i for i, col in enumerate(valid_columns) if col]
-            default_csv_param['usecols'] = valid_columns
+            default_csv_param['usecols'] = transform_duplicated_col_suffix_to_pandas_col(
+                dic_valid_csv_cols,
+                dic_org_csv_cols,
+            )
             use_col_names = [col for col in valid_columns if col]
 
         # read csv file
@@ -318,7 +336,9 @@ def import_csv(proc_id, record_per_commit=RECORD_PER_COMMIT, is_user_request=Non
         }
 
         if is_v2_datasource:
-            datasource_type, is_abnormal_v2 = get_v2_datasource_type_from_file(transformed_file)
+            datasource_type, is_abnormal_v2, is_en_cols = get_v2_datasource_type_from_file(
+                transformed_file
+            )
             if datasource_type == DBType.V2_HISTORY:
                 df_one_file = get_df_v2_process_single_file(
                     transformed_file,
@@ -332,6 +352,7 @@ def import_csv(proc_id, record_per_commit=RECORD_PER_COMMIT, is_user_request=Non
                     process_name=data_src.process_name,
                     datasource_type=datasource_type,
                     is_abnormal_v2=is_abnormal_v2,
+                    is_en_cols=is_en_cols,
                 )
             else:
                 continue
diff --git a/ap/api/setting_module/services/show_latest_record.py b/ap/api/setting_module/services/show_latest_record.py
index 301688a..a70ee5c 100644
--- a/ap/api/setting_module/services/show_latest_record.py
+++ b/ap/api/setting_module/services/show_latest_record.py
@@ -110,7 +110,7 @@ def get_latest_records(data_source_id, table_name, limit):
                 return_df=True,
                 max_records=1000,
             )
-        headers = dic_preview.get('header')
+        headers = normalize_list(dic_preview.get('header'))
         data_types = dic_preview.get('dataType')
         same_values = dic_preview.get('same_values')
         is_v2_history = dic_preview.get('v2_type') == DBType.V2_HISTORY
@@ -118,8 +118,9 @@ def get_latest_records(data_source_id, table_name, limit):
             cols_with_types = gen_cols_with_types(headers, data_types, same_values, is_v2_history)
 
         # sort columns
-        sorted_columns = sorted(csv_detail.csv_columns, key=lambda c: c.order or c.id)
-        cols = [col.column_name for col in sorted_columns if col.column_name in headers]
+        sorted(csv_detail.csv_columns, key=lambda c: c.order or c.id)
+        # cols = {col.column_name for col in sorted_columns if col.column_name in headers}
+        cols = headers
 
         # get rows
         df_rows = dic_preview.get('content', None)
@@ -495,7 +496,9 @@ def preview_v2_data(folder_url, csv_delimiter, limit, return_df=False, process_n
         file_data_idx = 0
         while file_data_idx >= 0:
             largest_file = sorted_files[file_data_idx]
-            datasource_type, is_abnormal_v2 = get_v2_datasource_type_from_file(largest_file)
+            datasource_type, is_abnormal_v2, is_en_cols = get_v2_datasource_type_from_file(
+                largest_file
+            )
 
             if datasource_type == DBType.V2_HISTORY:
                 data_details = get_df_v2_process_single_file(
@@ -503,7 +506,7 @@ def preview_v2_data(folder_url, csv_delimiter, limit, return_df=False, process_n
                 )
             elif datasource_type in [DBType.V2, DBType.V2_MULTI]:
                 data_details = get_vertical_df_v2_process_single_file(
-                    largest_file, process_name, datasource_type, is_abnormal_v2
+                    largest_file, process_name, datasource_type, is_abnormal_v2, is_en_cols
                 )
             else:
                 raise NotImplementedError
@@ -550,7 +553,7 @@ def preview_v2_data(folder_url, csv_delimiter, limit, return_df=False, process_n
 
     # get DB Type and check if there is abnormal history
     if is_abnormal_v2 is None and not datasource_type:
-        datasource_type, is_abnormal_v2 = get_v2_datasource_type_from_file(csv_file)
+        datasource_type, is_abnormal_v2, _ = get_v2_datasource_type_from_file(csv_file)
     header_names = rename_abnormal_history_col_names(datasource_type, header_names, is_abnormal_v2)
     org_headers, header_names, dupl_cols = gen_colsname_for_duplicated(header_names)
     df_data_details = normalize_big_rows(data_details, header_names)
@@ -626,6 +629,7 @@ def preview_v2_data(folder_url, csv_delimiter, limit, return_df=False, process_n
         'has_dupl_cols': has_dupl_cols,
         'org_headers': org_headers,
         'v2_type': datasource_type.value,
+        'is_process_null': not v2_process_names,
     }
 
 
@@ -744,6 +748,7 @@ def convert_utc_df(df_rows, cols, data_types, data_source, table_name):
 
 
 def transform_df_to_rows(cols, df_rows, limit):
+    df_rows.columns = normalize_list(df_rows.columns)
     return [
         dict(zip(cols, vals)) for vals in df_rows[0:limit][cols].to_records(index=False).tolist()
     ]
@@ -768,7 +773,7 @@ def gen_preview_data_check_dict(rows, previewed_files):
 @log_execution_time()
 def gen_colsname_for_duplicated(cols_name):
     org_cols_name = cols_name.copy()
-    cols_name, dup_cols = chw.add_suffix_if_duplicated(cols_name, True, True)
+    cols_name, dup_cols = chw.add_suffix_if_duplicated(cols_name, True)
     return org_cols_name, cols_name, dup_cols
 
 
diff --git a/ap/api/setting_module/services/v2_etl_services.py b/ap/api/setting_module/services/v2_etl_services.py
index 9621579..05cd01d 100644
--- a/ap/api/setting_module/services/v2_etl_services.py
+++ b/ap/api/setting_module/services/v2_etl_services.py
@@ -2,7 +2,7 @@
 import logging
 import re
 from pathlib import Path
-from typing import List, Optional, Tuple, Union
+from typing import Any, List, Optional, Tuple, Union
 
 import numpy as np
 import pandas as pd
@@ -16,11 +16,14 @@
     ABNORMAL_V2_COLS,
     ABNORMAL_WELL_KNOWN_COLUMNS,
     DF_CHUNK_SIZE,
+    DUMMY_V2_PROCESS_NAME,
     REVERSED_WELL_KNOWN_COLUMNS,
+    REVERSED_WELL_KNOWN_EN_COLUMNS,
     SUB_PART_NO_DEFAULT_NO,
     SUB_PART_NO_DEFAULT_SUFFIX,
     SUB_PART_NO_NAMES,
     WELL_KNOWN_COLUMNS,
+    WELL_KNOWN_EN_COLUMNS,
     DataGroupType,
     DataType,
     DBType,
@@ -29,6 +32,7 @@
 from ap.common.logger import log_execution_time
 from ap.common.memoize import memoize
 from ap.common.services.csv_content import gen_data_types, get_metadata
+from ap.common.services.csv_header_wrapr import add_suffix_if_duplicated
 from ap.common.services.jp_to_romaji_utils import to_romaji
 from ap.common.services.normalization import normalize_str
 from ap.setting_module.models import (
@@ -124,14 +128,14 @@ def get_preview_processes_v2(
     found_processes = set()
     sorted_files = sorted_files[:maximum_files]
     for f_name in sorted_files:
-        datasource_type, is_abnormal_v2 = get_v2_datasource_type_from_file(f_name)
+        datasource_type, is_abnormal_v2, is_en_cols = get_v2_datasource_type_from_file(f_name)
         if datasource_type not in [DBType.V2, DBType.V2_MULTI, DBType.V2_HISTORY]:
             continue
 
         process_col_name = get_reversed_column_value_from_v2(
-            datasource_type.name, DataGroupType.PROCESS_NAME.value, is_abnormal_v2
+            datasource_type.name, DataGroupType.PROCESS_NAME.value, is_abnormal_v2, is_en_cols
         )
-        params = build_read_csv_for_v2(f_name, datasource_type, is_abnormal_v2)
+        params = build_read_csv_for_v2(f_name, datasource_type, is_abnormal_v2, is_en_cols)
         # we only use process_col_name
         params.update(usecols=[process_col_name])
         try:
@@ -155,25 +159,32 @@ def get_preview_processes_v2(
 @log_execution_time()
 @memoize()
 def get_df_v2_process_single_file(
-    v2_file: str, process_name: str, datasource_type=None, is_abnormal_v2=False
+    v2_file: str,
+    process_name: str,
+    datasource_type=None,
+    is_abnormal_v2=False,
+    is_en_cols=False,
 ) -> DataFrame:
     df = pd.DataFrame()
 
     if not datasource_type:
-        datasource_type, is_abnormal_v2 = get_v2_datasource_type_from_file(v2_file)
+        datasource_type, is_abnormal_v2, is_en_cols = get_v2_datasource_type_from_file(v2_file)
     assert datasource_type in [
         DBType.V2,
         DBType.V2_MULTI,
         DBType.V2_HISTORY,
     ], 'We only need to get process from v2'
     process_col_name = get_reversed_column_value_from_v2(
-        datasource_type.name, DataGroupType.PROCESS_NAME.value, is_abnormal_v2
+        datasource_type.name, DataGroupType.PROCESS_NAME.value, is_abnormal_v2, is_en_cols
     )
-    params = build_read_csv_for_v2(v2_file, datasource_type, is_abnormal_v2)
+    params = build_read_csv_for_v2(v2_file, datasource_type, is_abnormal_v2, is_en_cols)
     try:
         with pd.read_csv(v2_file, chunksize=DF_CHUNK_SIZE, **params) as reader:
             for df_chunk in reader:
-                df_process = df_chunk[df_chunk[process_col_name] == process_name]
+                if process_name == DUMMY_V2_PROCESS_NAME:
+                    df_process = df_chunk[df_chunk[process_col_name].isna()]
+                else:
+                    df_process = df_chunk[df_chunk[process_col_name] == process_name]
                 df_process = df_process.drop_duplicates()
                 df = pd.concat([df, df_process])
                 df = df.drop_duplicates()
@@ -182,7 +193,10 @@ def get_df_v2_process_single_file(
             v2_file, chunksize=DF_CHUNK_SIZE, quoting=csv.QUOTE_NONE, **params
         ) as reader:
             for df_chunk in reader:
-                df_process = df_chunk[df_chunk[process_col_name] == process_name]
+                if process_name == DUMMY_V2_PROCESS_NAME:
+                    df_process = df_chunk[df_chunk[process_col_name].isna()]
+                else:
+                    df_process = df_chunk[df_chunk[process_col_name] == process_name]
                 df_process = df_process.drop_duplicates()
                 df = pd.concat([df, df_process])
                 df = df.drop_duplicates()
@@ -191,7 +205,7 @@ def get_df_v2_process_single_file(
         pass
 
     # rename abnormal history name
-    if is_abnormal_v2:
+    if is_abnormal_v2 and datasource_type == DBType.V2_HISTORY:
         df = rename_abnormal_history_col_names_from_df(df, datasource_type)
     return df
 
@@ -208,40 +222,55 @@ def get_df_v2_process_multiple_files(v2_files: List[str], process_name: str) ->
 
 
 @log_execution_time()
-def simple_convert_to_v2_vertical(df: DataFrame, datasource_type=None) -> DataFrame:
+def simple_convert_to_v2_vertical(
+    df: DataFrame, datasource_type=None, is_abnormal_v2=False, is_en_cols=False
+) -> DataFrame:
     if not datasource_type:
-        datasource_type, _ = get_v2_datasource_type_from_df(df)
+        datasource_type, is_abnormal_v2, is_en_cols = get_v2_datasource_type_from_df(df)
     assert datasource_type in [
         DBType.V2,
         DBType.V2_MULTI,
     ], 'We only need to convert vertical from v2 and v2_multi'
 
-    # TODO: the logic isn't the same as bridge, add more conditions later
-    all_columns = WELL_KNOWN_COLUMNS[datasource_type.name].keys()
-    quality_id_col = REVERSED_WELL_KNOWN_COLUMNS[datasource_type.name].get(
-        DataGroupType.QUALITY_ID.value
-    )
-    quality_name_col = REVERSED_WELL_KNOWN_COLUMNS[datasource_type.name].get(
-        DataGroupType.QUALITY_NAME.value
-    )
-    data_value_col = REVERSED_WELL_KNOWN_COLUMNS[datasource_type.name].get(
-        DataGroupType.DATA_VALUE.value
+    (
+        all_columns,
+        quality_id_col,
+        quality_name_col,
+        data_value_col,
+    ) = get_quality_and_data_value_from_v2(
+        datasource_type,
+        is_abnormal_v2,
+        is_en_cols,
     )
+
+    quality_name_like_cols = []
+    data_value_like_cols = []
+    normalized_cols = df.columns
     unique_cols = [
         col for col in all_columns if col not in [quality_name_col, data_value_col, quality_id_col]
     ]
+    if is_en_cols:
+        normalized_cols = normalize_column_name(normalized_cols)
 
-    quality_name_like_cols = [col for col in df.columns if col.startswith(quality_name_col)]
-
-    data_value_like_cols = []
-    for col in df.columns:
+    dict_normalized_cols = dict(zip(normalized_cols, df.columns))
+    for normalized_col, col in zip(normalized_cols, df.columns):
         # pandas will add suffix '.' to duplicated columns
-        if col == data_value_col or col.startswith(f'{data_value_col}.'):
+        if normalized_col == data_value_col or normalized_col.startswith(
+            (f'{data_value_col}.', f'{data_value_col}_')
+        ):
             data_value_like_cols.append(col)
 
+        if normalized_col.startswith(quality_name_col):
+            quality_name_like_cols.append(col)
+
     assert len(quality_name_like_cols) == len(data_value_like_cols)
     assert len(quality_name_like_cols) > 0
 
+    # get original columns name
+    quality_name_col = dict_normalized_cols[quality_name_col]
+    data_value_col = dict_normalized_cols[data_value_col]
+    unique_cols = [dict_normalized_cols[col] for col in unique_cols]
+
     # we don't need to melt if we don't have multiple quality_name + data_value columns
     if len(quality_name_like_cols) > 1:
         # TODO: use temporary column from constant
@@ -249,6 +278,7 @@ def simple_convert_to_v2_vertical(df: DataFrame, datasource_type=None) -> DataFr
         temp_id_column = '__id__'
 
         stub_columns_to_be_converted = [quality_name_col, data_value_col]
+
         # add ".0" since the first columns does not have this suffix
         mapping = {col: f'{col}.0' for col in stub_columns_to_be_converted}
         df = df.rename(columns=mapping)
@@ -270,16 +300,39 @@ def simple_convert_to_v2_vertical(df: DataFrame, datasource_type=None) -> DataFr
 
     df = df.drop_duplicates(subset=unique_cols + [quality_name_col])
     df = df.dropna(subset=[quality_name_col])
+    # replace vertical cols
+    unique_vertical_cols = df[quality_name_col].unique().tolist()
+    normalized_vertical_cols = (
+        pd.Series(unique_vertical_cols)
+        .apply(normalize_str)
+        .replace(r'計測値:|measurement.', '', regex=True)
+    )
+
     df = df.pivot(index=unique_cols, columns=quality_name_col, values=data_value_col)
+    # find duplicate quality columns, compare with index of df
+    duplicated_cols = list(set(df.columns).intersection(unique_cols))
+    filter_cols = unique_cols + list(normalized_vertical_cols)
+    if len(duplicated_cols) or len(filter_cols):
+        df_columns, is_duplicated = add_suffix_if_duplicated(filter_cols, skip_zero=True)
+        # rename columns if there is duplicated measure item name
+        if True in is_duplicated:
+            normalized_vertical_cols = df_columns[len(unique_cols) :]
+    df.rename(columns=dict(zip(unique_vertical_cols, normalized_vertical_cols)), inplace=True)
     return df.reset_index()
 
 
 @log_execution_time()
 def get_vertical_df_v2_process_single_file(
-    file: str, process_name: str, datasource_type=None, is_abnormal_v2=False
+    file: str,
+    process_name: str,
+    datasource_type=None,
+    is_abnormal_v2=False,
+    is_en_cols=False,
 ) -> DataFrame:
-    df = get_df_v2_process_single_file(file, process_name, datasource_type, is_abnormal_v2)
-    df = simple_convert_to_v2_vertical(df, datasource_type)
+    df = get_df_v2_process_single_file(
+        file, process_name, datasource_type, is_abnormal_v2, is_en_cols
+    )
+    df = simple_convert_to_v2_vertical(df, datasource_type, is_abnormal_v2, is_en_cols)
     return df
 
 
@@ -292,7 +345,7 @@ def get_vertical_df_v2_process_multiple_files(v2_files: List[str], process_name:
 
 @log_execution_time()
 def build_read_csv_for_v2(
-    file_path: str, datasource_type: DBType = DBType.V2, is_abnormal_v2=False
+    file_path: str, datasource_type: DBType = DBType.V2, is_abnormal_v2=False, is_en_cols=False
 ):
     from ap.api.setting_module.services.data_import import NA_VALUES
 
@@ -305,11 +358,21 @@ def build_read_csv_for_v2(
     must_get_columns = tuple(WELL_KNOWN_COLUMNS[datasource_type.name].keys())
     if is_abnormal_v2:
         must_get_columns = tuple(ABNORMAL_WELL_KNOWN_COLUMNS[datasource_type.name].keys())
-    usecols = lambda x: x.startswith(must_get_columns)
+
+    if is_en_cols:
+        must_get_columns = tuple(WELL_KNOWN_EN_COLUMNS[datasource_type.name].keys())
+
+    def usecols(x):
+        return x.startswith(must_get_columns)
+
+    def usecols_with_normalization(x):
+        [normalized_x] = normalize_column_name([x])
+        return normalized_x.startswith(must_get_columns)
+
     dtype = 'str'
     params.update(
         dict(
-            usecols=usecols,
+            usecols=usecols if not is_en_cols else usecols_with_normalization,
             skipinitialspace=True,
             na_values=NA_VALUES,
             error_bad_lines=False,
@@ -371,7 +434,7 @@ def rename_sub_part_no(df: pd.DataFrame, datasource_type=None) -> Tuple[DataFram
         part_no_columns.append(partno)
 
     if not datasource_type:
-        datasource_type, _ = get_v2_datasource_type_from_df(df)
+        datasource_type, *_ = get_v2_datasource_type_from_df(df)
 
     if datasource_type != DBType.V2_HISTORY:
         # v2 measure
@@ -435,25 +498,39 @@ def find_remaining_columns(process_id, all_columns):
     return [col for col in all_columns if col not in used_columns]
 
 
-def get_v2_datasource_type_from_file(v2_file: Union[Path, str]) -> Optional[DBType]:
+def get_v2_datasource_type_from_file(v2_file: Union[Path, str]) -> tuple[Any, Any, Any]:
     """Check if this file is v2, v2 multi or v2 history"""
     df = pd.read_csv(v2_file, nrows=1)
-    return get_v2_datasource_type_from_df(df)
+    datasource_type, is_abnormal, is_en_cols = get_v2_datasource_type_from_df(df)
+    return datasource_type, is_abnormal, is_en_cols
 
 
-def get_v2_datasource_type_from_df(df: DataFrame) -> Tuple[Optional[DBType], bool]:
+def get_v2_datasource_type_from_df(
+    df: DataFrame,
+) -> Union[tuple[DBType, bool, bool], tuple[None, bool, bool]]:
     columns = set(col.strip() for col in df.columns)
     is_abnormal = False
+    is_en_cols = False
     for datasource_type in [DBType.V2_HISTORY, DBType.V2, DBType.V2_MULTI]:
         must_exist_columns = set(WELL_KNOWN_COLUMNS[datasource_type.name].keys())
         abnormal_must_exist_columns = set(ABNORMAL_WELL_KNOWN_COLUMNS[datasource_type.name].keys())
+        en_must_exist_columns = (
+            set(WELL_KNOWN_EN_COLUMNS[datasource_type.name].keys())
+            if datasource_type.name in WELL_KNOWN_EN_COLUMNS
+            else ()
+        )
         if columns >= must_exist_columns:
-            return datasource_type, is_abnormal
+            return datasource_type, is_abnormal, is_en_cols
 
         if columns >= abnormal_must_exist_columns:
             is_abnormal = True
-            return datasource_type, is_abnormal
-    return None, is_abnormal
+            return datasource_type, is_abnormal, is_en_cols
+
+        if len(en_must_exist_columns):
+            normalize_cols = set(normalize_column_name(columns))
+            if normalize_cols >= en_must_exist_columns:
+                return datasource_type, is_abnormal, True
+    return None, is_abnormal, is_en_cols
 
 
 @log_execution_time()
@@ -483,7 +560,7 @@ def prepare_to_import_v2_df(
     :return: transformed dataframe, has_new_columns
     """
     if not datasource_type:
-        datasource_type, _ = get_v2_datasource_type_from_df(df)
+        datasource_type, *_ = get_v2_datasource_type_from_df(df)
 
     col_names = {col: normalize_str(col) for col in df.columns}
     df = df.rename(columns=col_names)
@@ -497,13 +574,18 @@ def prepare_to_import_v2_df(
     return df, has_remaining_cols
 
 
-def get_reversed_column_value_from_v2(datasource_type, reversed_column_name, is_abnormal_v2):
+def get_reversed_column_value_from_v2(
+    datasource_type, reversed_column_name, is_abnormal_v2, is_en_cols=False
+):
     """
     :return: v2 normal column name
     """
     if is_abnormal_v2:
         return ABNORMAL_REVERSED_WELL_KNOWN_COLUMNS[datasource_type][reversed_column_name]
 
+    if is_en_cols:
+        return REVERSED_WELL_KNOWN_EN_COLUMNS[datasource_type][reversed_column_name]
+
     return REVERSED_WELL_KNOWN_COLUMNS[datasource_type][reversed_column_name]
 
 
@@ -539,3 +621,62 @@ def rename_abnormal_history_col_names_from_df(df, datasource_type):
     if len(rename_headers.keys()):
         df.rename(columns=rename_headers, inplace=True)
     return df
+
+
+def normalize_column_name(columns_name):
+    # define to convert these symbols to underscore
+    convert_symbols = ['.', '/', ' ', '-']
+    normalize_cols = []
+    for column_name in columns_name:
+        col_name = column_name.lower()
+        for symbol in convert_symbols:
+            col_name = col_name.replace(symbol, '_')
+        if col_name[-1] == '_':
+            # remove last underscore of column name
+            # eg. serial_no_ -> serial_no
+            col_name = col_name[:-1]
+        normalize_cols.append(col_name)
+    return normalize_cols
+
+
+def get_quality_and_data_value_from_v2(datasource_type, is_abnormal_v2=False, is_en_cols=False):
+    """
+    :return: v2 quality and data value column
+    """
+    all_columns = WELL_KNOWN_COLUMNS[datasource_type.name].keys()
+    quality_id_col = REVERSED_WELL_KNOWN_COLUMNS[datasource_type.name].get(
+        DataGroupType.QUALITY_ID.value
+    )
+    quality_name_col = REVERSED_WELL_KNOWN_COLUMNS[datasource_type.name].get(
+        DataGroupType.QUALITY_NAME.value
+    )
+    data_value_col = REVERSED_WELL_KNOWN_COLUMNS[datasource_type.name].get(
+        DataGroupType.DATA_VALUE.value
+    )
+
+    # for v2 which column is en.
+    if is_en_cols:
+        all_columns = WELL_KNOWN_EN_COLUMNS[datasource_type.name].keys()
+        quality_id_col = REVERSED_WELL_KNOWN_EN_COLUMNS[datasource_type.name].get(
+            DataGroupType.QUALITY_ID.value
+        )
+        quality_name_col = REVERSED_WELL_KNOWN_EN_COLUMNS[datasource_type.name].get(
+            DataGroupType.QUALITY_NAME.value
+        )
+        data_value_col = REVERSED_WELL_KNOWN_EN_COLUMNS[datasource_type.name].get(
+            DataGroupType.DATA_VALUE.value
+        )
+
+    if is_abnormal_v2:
+        all_columns = ABNORMAL_WELL_KNOWN_COLUMNS[datasource_type.name].keys()
+        quality_id_col = ABNORMAL_REVERSED_WELL_KNOWN_COLUMNS[datasource_type.name].get(
+            DataGroupType.QUALITY_ID.value
+        )
+        quality_name_col = ABNORMAL_REVERSED_WELL_KNOWN_COLUMNS[datasource_type.name].get(
+            DataGroupType.QUALITY_NAME.value,
+        )
+        data_value_col = ABNORMAL_REVERSED_WELL_KNOWN_COLUMNS[datasource_type.name].get(
+            DataGroupType.DATA_VALUE.value
+        )
+
+    return all_columns, quality_id_col, quality_name_col, data_value_col
diff --git a/ap/common/constants.py b/ap/common/constants.py
index d80e7cf..585cd51 100644
--- a/ap/common/constants.py
+++ b/ap/common/constants.py
@@ -60,6 +60,8 @@
 AS_HEATMAP_MATRIX = 'as_heatmap_matrix'
 HEATMAP_MATRIX = 'heatmap_matrix'
 
+DUMMY_V2_PROCESS_NAME = 'DUMMY_V2_PROCESS_NAME'
+
 
 class ApLogLevel(Enum):
     DEBUG = auto()
@@ -371,6 +373,7 @@ class ErrorMsg(Enum):
 CELL_SUFFIX = '_cell'
 AGG_COL = 'agg_col'
 TIME_COL = 'time'
+TIME_COL_LOCAL = 'time_local'
 
 REQUEST_THREAD_ID = 'thread_id'
 SERIALS = 'serials'
@@ -940,29 +943,35 @@ class DataGroupType(BaseEnum):
     SUB_LOT_NO = 15
     SUB_TRAY_NO = 16
     SUB_SERIAL = 17
-    # generate equation
-    Femto_Date = 18
-    Femto_Mach = 19
-    Femto_Order = 20
-    Line = 21
-    Datetime = 22
-    Milling = 23
-
-    FACTORY_ID = 24
-    FACTORY_NAME = 25
-    PLANT_ID = 26
-    PLANT_NO = 27
-    DEPT_ID = 28
-    DEPT_NAME = 29
-    LINE_GROUP_ID = 30
-    LINE_GROUP_NAME = 31
-    PART_FULL = 32
-    EQUIP_ID = 33  # TODO CHECK
-    HORIZONTAL_DATA = 34  # Type for horizontal columns that are sensor columns
-
-    # PART_LOG
-    FORGING_DATE = 35
-    DELIVERY_ASSY_FASTEN_TORQUE = 36
+
+    # add new columns
+    WORK_TYPE = 18
+    QUALITY = 19
+    LOT_NO = 20
+    TRAY_NO = 21
+    # # generate equation
+    # Femto_Date = 18
+    # Femto_Mach = 19
+    # Femto_Order = 20
+    # Line = 21
+    # Datetime = 22
+    # Milling = 23
+    #
+    # FACTORY_ID = 24
+    # FACTORY_NAME = 25
+    # PLANT_ID = 26
+    # PLANT_NO = 27
+    # DEPT_ID = 28
+    # DEPT_NAME = 29
+    # LINE_GROUP_ID = 30
+    # LINE_GROUP_NAME = 31
+    # PART_FULL = 32
+    # EQUIP_ID = 33  # TODO CHECK
+    # HORIZONTAL_DATA = 34  # Type for horizontal columns that are sensor columns
+    #
+    # # PART_LOG
+    # FORGING_DATE = 35
+    # DELIVERY_ASSY_FASTEN_TORQUE = 36
 
     # PRODUCT_ID = 35
 
@@ -1001,6 +1010,9 @@ def get_all_reserved_groups(cls):
         '子設備ID': DataGroupType.MACHINE_ID.value,
         '子設備名': DataGroupType.MACHINE_NAME.value,
         '品番': DataGroupType.PART_NO.value,
+        'ワーク種別': DataGroupType.WORK_TYPE.value,
+        'ロットNo': DataGroupType.LOT_NO.value,
+        'トレイNo': DataGroupType.TRAY_NO.value,
         'シリアルNo': DataGroupType.DATA_SERIAL.value,
         '計測日時': DataGroupType.DATA_TIME.value,
         '計測項目ID': DataGroupType.QUALITY_ID.value,
@@ -1015,6 +1027,10 @@ def get_all_reserved_groups(cls):
         '子設備ID': DataGroupType.MACHINE_ID.value,
         '子設備': DataGroupType.MACHINE_NAME.value,
         '品番': DataGroupType.PART_NO.value,
+        'ワーク種別': DataGroupType.WORK_TYPE.value,
+        '良否': DataGroupType.QUALITY.value,
+        'ロットNo': DataGroupType.LOT_NO.value,
+        'トレイNo': DataGroupType.TRAY_NO.value,
         'シリアルNo': DataGroupType.DATA_SERIAL.value,
         '加工日時': DataGroupType.DATA_TIME.value,
         '測定項目名': DataGroupType.QUALITY_NAME.value,
@@ -1028,6 +1044,9 @@ def get_all_reserved_groups(cls):
         '子設備ID': DataGroupType.MACHINE_ID.value,
         '子設備': DataGroupType.MACHINE_NAME.value,
         '品番': DataGroupType.PART_NO.value,
+        'ワーク種別': DataGroupType.WORK_TYPE.value,
+        'ロットNo': DataGroupType.LOT_NO.value,
+        'トレイNo': DataGroupType.TRAY_NO.value,
         'シリアルNo': DataGroupType.DATA_SERIAL.value,
         '加工日時': DataGroupType.DATA_TIME.value,
         '子部品品番': DataGroupType.SUB_PART_NO.value,
@@ -1046,6 +1065,9 @@ def get_all_reserved_groups(cls):
         DataGroupType.MACHINE_ID.value: '子設備ID',
         DataGroupType.MACHINE_NAME.value: '子設備名',
         DataGroupType.PART_NO.value: '品番',
+        DataGroupType.WORK_TYPE.value: 'ワーク種別',
+        DataGroupType.LOT_NO.value: 'ロットNo',
+        DataGroupType.TRAY_NO.value: 'トレイNo',
         DataGroupType.DATA_SERIAL.value: 'シリアルNo',
         DataGroupType.DATA_TIME.value: '計測日時',
         DataGroupType.QUALITY_ID.value: '計測項目ID',
@@ -1061,6 +1083,10 @@ def get_all_reserved_groups(cls):
         DataGroupType.MACHINE_NAME.value: '子設備',
         DataGroupType.PART_NO.value: '品番',
         DataGroupType.DATA_SERIAL.value: 'シリアルNo',
+        DataGroupType.WORK_TYPE.value: 'ワーク種別',
+        DataGroupType.QUALITY.value: '良否',
+        DataGroupType.LOT_NO.value: 'ロットNo',
+        DataGroupType.TRAY_NO.value: 'トレイNo',
         DataGroupType.DATA_TIME.value: '加工日時',
         DataGroupType.QUALITY_NAME.value: '測定項目名',
         DataGroupType.DATA_VALUE.value: '測定値',
@@ -1073,6 +1099,9 @@ def get_all_reserved_groups(cls):
         DataGroupType.MACHINE_ID.value: '子設備ID',
         DataGroupType.MACHINE_NAME.value: '子設備',
         DataGroupType.PART_NO.value: '品番',
+        DataGroupType.WORK_TYPE.value: 'ワーク種別',
+        DataGroupType.LOT_NO.value: 'ロットNo',
+        DataGroupType.TRAY_NO.value: 'トレイNo',
         DataGroupType.DATA_SERIAL.value: 'シリアルNo',
         DataGroupType.DATA_TIME.value: '加工日時',
         DataGroupType.SUB_PART_NO.value: '子部品品番',
@@ -1090,6 +1119,9 @@ def get_all_reserved_groups(cls):
         '子設備ID': DataGroupType.MACHINE_ID.value,
         '子設備名': DataGroupType.MACHINE_NAME.value,
         '品番': DataGroupType.PART_NO.value,
+        'ワーク種別': DataGroupType.WORK_TYPE.value,
+        'ロット番号': DataGroupType.LOT_NO.value,
+        'トレイ番号': DataGroupType.TRAY_NO.value,
         'シリアル番号': DataGroupType.DATA_SERIAL.value,
         '計測日時': DataGroupType.DATA_TIME.value,
         '計測項目ID': DataGroupType.QUALITY_ID.value,
@@ -1104,6 +1136,10 @@ def get_all_reserved_groups(cls):
         '子設備ID': DataGroupType.MACHINE_ID.value,
         '子設備': DataGroupType.MACHINE_NAME.value,
         '品番': DataGroupType.PART_NO.value,
+        'ワーク種別': DataGroupType.WORK_TYPE.value,
+        '良否結果': DataGroupType.QUALITY.value,
+        'ロットNo': DataGroupType.LOT_NO.value,
+        'トレイNo': DataGroupType.TRAY_NO.value,
         'シリアルNo': DataGroupType.DATA_SERIAL.value,
         '加工日時': DataGroupType.DATA_TIME.value,
         '測定項目名': DataGroupType.QUALITY_NAME.value,
@@ -1117,6 +1153,9 @@ def get_all_reserved_groups(cls):
         '子設備ID': DataGroupType.MACHINE_ID.value,
         '子設備名': DataGroupType.MACHINE_NAME.value,
         '品番': DataGroupType.PART_NO.value,
+        'ワーク種別': DataGroupType.WORK_TYPE.value,
+        'ロットNo': DataGroupType.LOT_NO.value,
+        'トレイNo': DataGroupType.TRAY_NO.value,
         'シリアルNo': DataGroupType.DATA_SERIAL.value,
         '計測日時': DataGroupType.DATA_TIME.value,
         '子部品品番': DataGroupType.SUB_PART_NO.value,
@@ -1125,6 +1164,7 @@ def get_all_reserved_groups(cls):
         '子部品シリアルNo': DataGroupType.SUB_SERIAL.value,
     },
 }
+
 ABNORMAL_REVERSED_WELL_KNOWN_COLUMNS = {
     DBType.V2.name: {
         DataGroupType.LINE_ID.value: 'ラインID',
@@ -1134,6 +1174,9 @@ def get_all_reserved_groups(cls):
         DataGroupType.MACHINE_ID.value: '子設備ID',
         DataGroupType.MACHINE_NAME.value: '子設備名',
         DataGroupType.PART_NO.value: '品番',
+        DataGroupType.WORK_TYPE.value: 'ワーク種別',
+        DataGroupType.LOT_NO.value: 'ロット番号',
+        DataGroupType.TRAY_NO.value: 'トレイ番号',
         DataGroupType.DATA_SERIAL.value: 'シリアル番号',
         DataGroupType.DATA_TIME.value: '計測日時',
         DataGroupType.QUALITY_ID.value: '計測項目ID',
@@ -1148,6 +1191,10 @@ def get_all_reserved_groups(cls):
         DataGroupType.MACHINE_ID.value: '子設備ID',
         DataGroupType.MACHINE_NAME.value: '子設備',
         DataGroupType.PART_NO.value: '品番',
+        DataGroupType.WORK_TYPE.value: 'ワーク種別',
+        DataGroupType.QUALITY.value: '良否結果',
+        DataGroupType.LOT_NO.value: 'ロットNo',
+        DataGroupType.TRAY_NO.value: 'トレイNo',
         DataGroupType.DATA_SERIAL.value: 'シリアルNo',
         DataGroupType.DATA_TIME.value: '加工日時',
         DataGroupType.QUALITY_NAME.value: '測定項目名',
@@ -1161,6 +1208,9 @@ def get_all_reserved_groups(cls):
         DataGroupType.MACHINE_ID.value: '子設備ID',
         DataGroupType.MACHINE_NAME.value: '子設備名',
         DataGroupType.PART_NO.value: '品番',
+        DataGroupType.WORK_TYPE.value: 'ワーク種別',
+        DataGroupType.LOT_NO.value: 'ロットNo',
+        DataGroupType.TRAY_NO.value: 'トレイNo',
         DataGroupType.DATA_SERIAL.value: 'シリアルNo',
         DataGroupType.DATA_TIME.value: '計測日時',
         DataGroupType.SUB_PART_NO.value: '子部品品番',
@@ -1175,6 +1225,48 @@ def get_all_reserved_groups(cls):
     '子設備名': DataGroupType.MACHINE_NAME.value,
     '計測日時': DataGroupType.DATA_TIME.value,
     'シリアル番号': DataGroupType.DATA_SERIAL.value,
+    'ロット番号': DataGroupType.LOT_NO.value,
+    'トレイ番号': DataGroupType.TRAY_NO.value,
+}
+
+# for en column name from v2 files
+WELL_KNOWN_EN_COLUMNS = {
+    DBType.V2_MULTI.name: {
+        'line_id': DataGroupType.LINE_ID.value,
+        'line': DataGroupType.LINE_NAME.value,
+        'process_id': DataGroupType.PROCESS_ID.value,
+        'process': DataGroupType.PROCESS_NAME.value,
+        'equipment_id': DataGroupType.MACHINE_ID.value,
+        'equipment': DataGroupType.MACHINE_NAME.value,
+        'part_number': DataGroupType.PART_NO.value,
+        'work_type': DataGroupType.WORK_TYPE.value,
+        'quality': DataGroupType.QUALITY.value,
+        'lot_no': DataGroupType.LOT_NO.value,
+        'tray_no': DataGroupType.TRAY_NO.value,
+        'serial_no': DataGroupType.DATA_SERIAL.value,
+        'processed_date_time': DataGroupType.DATA_TIME.value,
+        'measurement_item_name': DataGroupType.QUALITY_NAME.value,
+        'measured_value': DataGroupType.DATA_VALUE.value,
+    },
+}
+REVERSED_WELL_KNOWN_EN_COLUMNS = {
+    DBType.V2_MULTI.name: {
+        DataGroupType.LINE_ID.value: 'line_id',
+        DataGroupType.LINE_NAME.value: 'line',
+        DataGroupType.PROCESS_ID.value: 'process_id',
+        DataGroupType.PROCESS_NAME.value: 'process',
+        DataGroupType.MACHINE_ID.value: 'equipment_id',
+        DataGroupType.MACHINE_NAME.value: 'equipment',
+        DataGroupType.PART_NO.value: 'part_number',
+        DataGroupType.WORK_TYPE.value: 'work_type',
+        DataGroupType.QUALITY.value: 'quality',
+        DataGroupType.LOT_NO.value: 'lot_no',
+        DataGroupType.TRAY_NO.value: 'tray_no',
+        DataGroupType.DATA_SERIAL.value: 'serial_no',
+        DataGroupType.DATA_TIME.value: 'processed_date_time',
+        DataGroupType.QUALITY_NAME.value: 'measurement_item_name',
+        DataGroupType.DATA_VALUE.value: 'measured_value',
+    },
 }
 
 SUB_PART_NO_DEFAULT_SUFFIX = '.'
diff --git a/ap/common/services/csv_content.py b/ap/common/services/csv_content.py
index 8a894b7..d902276 100644
--- a/ap/common/services/csv_content.py
+++ b/ap/common/services/csv_content.py
@@ -203,6 +203,17 @@ def check_data_type(data):
     except (ValueError, TypeError):
         pass
 
+    # try if there is not iso format of datetime
+    # eg: 20-09-2023 01:00
+    try:
+        is_datetime = datetime.fromisoformat(data)
+        if is_datetime:
+            return DataType.DATETIME
+    except (ValueError, TypeError):
+        valid_dt = detect_datetime(data)
+        if valid_dt:
+            return DataType.DATETIME
+
     return predict_eu_type(data)
 
 
@@ -360,3 +371,12 @@ def zip_file_to_response(csv_data, file_names, export_type='csv'):
 
     response.charset = encoding
     return response
+
+
+def detect_datetime(datetime_value):
+    try:
+        # try to read value as datetime
+        pd.to_datetime([datetime_value])
+        return True
+    except (ValueError, TypeError):
+        return False
diff --git a/ap/common/services/csv_header_wrapr.py b/ap/common/services/csv_header_wrapr.py
index fd8391d..2d49290 100644
--- a/ap/common/services/csv_header_wrapr.py
+++ b/ap/common/services/csv_header_wrapr.py
@@ -540,7 +540,7 @@ def summarize_header_as_df(hdr: dict, info: dict):
     # translate
     head['main'] = _translate_wellknown_jp2en(head['main'])
     # if head$main has some same value, add _01, _02, ...
-    head['main'] = add_suffix_if_duplicated(head['main'])
+    head['main'], _ = add_suffix_if_duplicated(head['main'])
 
     df_head = pd.DataFrame(head, index=head['main'])
     return df_head
@@ -601,13 +601,11 @@ def _translate_wellknown_jp2en(x):
     return x
 
 
-def add_suffix_if_duplicated(x, skip_zero=False, with_dupl_cols=False):
-    is_dupl_cols = [False] * len(x)
-    duplicated = [k for k, v in Counter(x).items() if v > 1]
+def add_suffix_if_duplicated(names, skip_zero=False):
+    is_dupl_cols = [False] * len(names)
+    duplicated = [k for k, v in Counter(names).items() if v > 1]
     if len(duplicated) == 0:
-        if with_dupl_cols:
-            return x, is_dupl_cols
-        return x
+        return names, is_dupl_cols
 
     if not skip_zero:
         # [a_01, a_02, a_03]
@@ -616,18 +614,35 @@ def add_suffix_if_duplicated(x, skip_zero=False, with_dupl_cols=False):
         # [a, a_01, a_02]
         suffix_format = (f'_{str(x - 1).zfill(2)!s}' if x > 1 else '' for x in range(1, 100))
     dic_suffix = dict(zip(duplicated, tee(suffix_format, len(duplicated))))
-    for idx, s in enumerate(x):
+    for idx, s in enumerate(names):
         try:
             suffix = str(next(dic_suffix[s]))
         except KeyError:
             continue
         else:
-            x[idx] += suffix
-            is_dupl_cols[idx] = True
+            names[idx] += suffix
+            if suffix:
+                is_dupl_cols[idx] = True
+
+    return names, is_dupl_cols
+
+
+def transform_duplicated_col_suffix_to_pandas_col(dic_valid_csv_cols, dic_original_cols):
+    col_names = []
+    for col_name, is_add_suffix in dic_valid_csv_cols.items():
+        org_col_name = col_name if not dic_original_cols else dic_original_cols[col_name]
+        if is_add_suffix:
+            # [a, a_01, a_02] -> [a, a.1, a.2]
+            matched = org_col_name.split('_')
+            if len(matched) > 1 and matched[-1].isdigit():
+                s = '_'.join(matched[0:-1])
+                col_names.append(f'{s}.{int(matched[-1])}')
+            else:
+                col_names.append(org_col_name)
+        else:
+            col_names.append(org_col_name)
 
-    if with_dupl_cols:
-        return x, is_dupl_cols
-    return x
+    return col_names
 
 
 # =========================
diff --git a/ap/setting_module/models.py b/ap/setting_module/models.py
index e94bc3c..7d946c3 100644
--- a/ap/setting_module/models.py
+++ b/ap/setting_module/models.py
@@ -591,11 +591,11 @@ def get_all_columns(cls, proc_id):
 
     @classmethod
     def get_columns_by_process_id(cls, proc_id):
-        return (
-            cls.query.filter(cls.process_id == proc_id)
-            .with_entities(cls.id, cls.name, cls.data_type)
-            .all()
-        )
+        columns = cls.query.filter(cls.process_id == proc_id).all()
+        return [
+            {cls.id.name: col.id, 'name': col.shown_name, cls.data_type.name: col.data_type}
+            for col in columns
+        ]
 
 
 class CfgProcess(db.Model):
@@ -807,7 +807,8 @@ def update_order(cls, meta_session, process_id, order):
 
     @classmethod
     def get_list_of_process(cls):
-        return cls.query.with_entities(cls.id, cls.name).all()
+        processes = cls.query.order_by(cls.id).all()
+        return [{cls.id.name: proc.id, cls.name.name: proc.shown_name} for proc in processes]
 
 
 class CfgTraceKey(db.Model):
diff --git a/ap/setting_module/schemas.py b/ap/setting_module/schemas.py
index 61e14e0..19c285a 100644
--- a/ap/setting_module/schemas.py
+++ b/ap/setting_module/schemas.py
@@ -126,6 +126,7 @@ class Meta:
     name_local = fields.String(required=False, allow_none=True)
     name_en = fields.String(required=False, allow_none=False)
     shown_name = fields.String(required=False, allow_none=True)
+    name = fields.String(required=False, allow_none=True)
 
     @post_load
     def make_obj(self, data, **kwargs):
diff --git a/ap/setting_module/services/process_config.py b/ap/setting_module/services/process_config.py
index 5bb8819..e18a081 100644
--- a/ap/setting_module/services/process_config.py
+++ b/ap/setting_module/services/process_config.py
@@ -95,7 +95,8 @@ def create_or_update_process_cfg(proc_data, unused_columns):
 
             proc_column.process_id = process.id
             # transform english name
-            proc_column.name_en = to_romaji(proc_column.column_name)
+            if not proc_column.name_en:
+                proc_column.name_en = to_romaji(proc_column.column_name)
 
             sensor = Sensor.get_sensor_by_col_name(process.id, proc_column.column_name)
 
diff --git a/ap/static/aggregate_plot/js/aggregate_plot.js b/ap/static/aggregate_plot/js/aggregate_plot.js
index 1c564d0..8ea217f 100644
--- a/ap/static/aggregate_plot/js/aggregate_plot.js
+++ b/ap/static/aggregate_plot/js/aggregate_plot.js
@@ -277,7 +277,10 @@ const collectInputAsFormData = (clearOnFlyFilter, autoUpdate = false) => {
                 formData.set(CYCLIC_TERM.DIV_OFFSET, offsetH.toString());
             }
 
-            formData.set('divDates', JSON.stringify(divFromTo));
+            // convert divFromTo from local to UTC
+            const divDates = divFromTo.map(date => toUTCDateTime(date, null, true));
+
+            formData.set('divDates', JSON.stringify(divDates));
             formData.set('divFormats', JSON.stringify(divFormats))
         }
 
diff --git a/ap/static/common/js/utils.js b/ap/static/common/js/utils.js
index 978580e..caedf0b 100644
--- a/ap/static/common/js/utils.js
+++ b/ap/static/common/js/utils.js
@@ -1374,11 +1374,15 @@ const syncTraceDateTimeRange = (parentId = '', dtNames = {}, dtValues = {}) => {
     }
 };
 
-const toUTCDateTime = (localDate, localTime) => {
-    if (isEmpty(localDate) || isEmpty(localTime)) return {date: localDate, time: localTime};
+const toUTCDateTime = (localDate, localTime, withDateTime = false) => {
+    if (!withDateTime && (isEmpty(localDate) || isEmpty(localTime))) return {date: localDate, time: localTime};
 
-    const utcDT = moment.utc(moment(`${localDate} ${localTime}`, `${DATE_FORMAT} ${TIME_FORMAT}`));
+    const datetime = withDateTime ? localDate : `${localDate} ${localTime}`;
+    const utcDT = moment.utc(moment(datetime, `${DATE_FORMAT} ${TIME_FORMAT}`));
     if (utcDT.isValid()) {
+        if (withDateTime) {
+            return utcDT.format(DATE_TIME_FMT);
+        }
         return {
             date: utcDT.format(DATE_FORMAT),
             time: utcDT.format(TIME_FORMAT),
diff --git a/ap/static/setting_module/js/db_config.js b/ap/static/setting_module/js/db_config.js
index 9ac97e6..f1c4feb 100644
--- a/ap/static/setting_module/js/db_config.js
+++ b/ap/static/setting_module/js/db_config.js
@@ -6,6 +6,7 @@ let v2DataSources = null;
 let MAX_NUMBER_OF_SENSOR = 100000000;
 let isV2ProcessConfigOpening = false;
 let v2ImportInterval = null;
+const DUMMY_V2_PROCESS_NAME = 'DUMMY_V2_PROCESS_NAME';
 // data type
 const originalTypes = {
     0: null,
@@ -319,10 +320,16 @@ const showResources = async () => {
             } else {
                 showLatestRecordsFromDS(res, true, true, !!res.v2_processes);
             }
+
+            if (isV2 && res.is_process_null) {
+                res.v2_processes = [DUMMY_V2_PROCESS_NAME];
+                res.v2_processes_shown_name = [$(dbConfigElements.csvDBSourceName).val()];
+            }
             // update process of V2
             if (res.v2_processes && res.v2_processes.length) {
                 const v2ProcessList = res.v2_processes;
-                addProcessList(v2ProcessList, v2ProcessList);
+                const v2ProcessShownNameList = res.v2_processes_shown_name || res.v2_processes;
+                addProcessList(v2ProcessList, v2ProcessShownNameList);
                 $('input[name="v2Process"]').on('change', () => {
                     const selectedProcess = getCheckedV2Processes();
                     if (selectedProcess.length) {
@@ -701,7 +708,7 @@ const saveCSVDataSource = (isV2=false) => {
             $(dbElements.saveDataSourceModal).modal('show');
             v2DataSources = v2DatasourceByProcess;
 
-        } else {
+        } else if (v2DatasourceByProcess.length == 1) {
             saveV2DataSource(v2DatasourceByProcess);
         }
     } else {
@@ -1703,7 +1710,8 @@ const getV2ProcessData = (dictDataSrc) => {
     if (v2SelectedProcess.length) {
         v2SelectedProcess.forEach(processName => {
             const subDatasourceByProcess = JSON.parse(JSON.stringify(dictDataSrc));
-            subDatasourceByProcess.name = `${subDatasourceByProcess.name}_${processName}`;
+            const suffix = processName === DUMMY_V2_PROCESS_NAME ? '' : `_${processName}`;
+            subDatasourceByProcess.name = `${subDatasourceByProcess.name}${suffix}`;
             subDatasourceByProcess.csv_detail.process_name = processName;
             subDatasourceByProcess.csv_detail.auto_link = false;
             v2Datasources.push(subDatasourceByProcess);
diff --git a/ap/static/setting_module/js/proc_config.js b/ap/static/setting_module/js/proc_config.js
index 5f4baca..ca3b2b7 100644
--- a/ap/static/setting_module/js/proc_config.js
+++ b/ap/static/setting_module/js/proc_config.js
@@ -14,8 +14,6 @@ const procElements = {
     divProcConfig: '#accordionPC',
 };
 
-const serialNo = ['シリアルNo', 'シリアル']
-
 const i18n = {
     statusDone: $('#i18nStatusDone').text(),
     statusImporting: $('#i18nStatusImporting').text(),
@@ -138,7 +136,7 @@ const genColConfigHTML = (col, isAddNew = true) => {
 
     // if v2 col_name is シリアルNo -> auto check
     if (!isSerial && isAddNew) {
-        isSerial = serialNo.includes(col.column_name) ? 'checked' : '';
+        isSerial = /^.*シリアル|serial.*$/.test(col.column_name.toString().toLowerCase()) ? 'checked' : '';
     }
 
     return `<tr name="selectedColumn" id="selectedColumn${col.column_name}" uid="${col.column_name}">
@@ -171,7 +169,7 @@ const genColConfigHTML = (col, isAddNew = true) => {
                 <label class="custom-control-label" for="autoIncrementColumn${col.column_name}"></label>
             </div>
         </td>
-        <td class="pr-row"><input name="${procModalElements.englishName}" class="form-control" type="text" value="${isDateTime && !isDummyDatetime ? 'Datetime' : col.name_en}"></td>
+        <td class="pr-row"><input name="${procModalElements.englishName}" class="form-control" type="text" value="${isDateTime && !isDummyDatetime && isAddNew ? 'Datetime' : col.name_en}"></td>
         <td class="pr-row"><input name="${procModalElements.japaneseName}" data-shown-name="1" class="form-control" type="text" value="${col.name_jp || ''}"></td>
         <td class="pr-row"><input name="${procModalElements.localName}" data-shown-name="1" class="form-control" type="text" value="${col.name_local || ''}"></td>
         <td class="pr-row">
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